Objective To explore the value of ovarian-adnexal reporting and data system(O-RADS)MRI score combined with tumor markers(CA125+HE4)in ovarian tumors.Methods Data from 223 patients with ovarian tumors confirmed by pathology were analyzed retrospectively,including 260 lesions.The Kappa test was used to assess the consistency of O-RADS MRI score between low and high seniority physician groups.The receiver operating characteristic(ROC)curve was drawn to analyze the diagnostic effi-cacy of O-RADS MRI score combined with tumor markers(CA125+HE4)in ovarian tumors.Results The Kappa value of the O-RADS MRI score between low and high seniority physician groups was 0.803[95%confidence interval(CI)0.746-0.860].The sensitivity based on O-RADS MRI score for distinguishing benign and malignant ovarian tumors was 0.957 and 0.989,the specificity was 0.784 and 0.820,the accuracy was 0.846 and 0.881,the positive predictive value was 0.712 and 0.754,and the negative pre-dictive value was 0.970 and 0.993,and the area under the curve(AUC)was 0.871 and 0.905,respectively in the low and high senior-ity physician groups.Combined with tumor markers(CA125+HE4),the sensitivity and negative predictive value of both low and high seniority physician groups were 1.000.Conclusion The O-RADS MRI score has high diagnostic efficacy and good repeatability in ovarian tumors.Combined with tumor markers CA125 and HE4,the O-RADS MRI score can further improve the diagnostic sen-sitivity.

A retrospective analysis was conducted on data from five patients comprehensively diagnosed with emphysematous osteomyelitis (EO) based on clinical, imaging, microbiological culture, and surgical findings at Hebei Medical University Third Hospital from December 2020 to May 2024. Among these five cases, there were three males and two females, aged between 11 and 69 years. Three patients had infection risk factors (two with diabetes and one with anemia), while two had no documented risk factors in their medical history. All patients presented with fever, localized pain at the infection site, and elevated inflammatory markers. Site of incidence: EO affected the lumbar spine in three cases, the ilium in one case, and the femur in one case. Pathogenic microorganisms: The causative agents included Klebsiella pneumoniae (two cases), Escherichia coli (one case), Burkholderia cepacia (one case), and a mixed infection of Staphylococcus aureus and Acinetobacter baumannii (one case). Imaging findings: Among the three patients who underwent X-ray examinations, one showed normal results, while two exhibited bone destruction. CT scans of all five cases revealed the characteristic "pumice sign" without periosteal reaction or osteosclerosis. MRI, performed on four patients, demonstrated bone destruction, swelling of surrounding soft tissues, and soft tissue abscess formation. Treatment and outcomes: Four patients underwent surgical debridement combined with perioperative antibiotic therapy. Of these, three recovered well, while one achieved good infection control but suffered severe joint destruction. One patient died after treatment was discontinued. The clinical and laboratory findings of EO resemble those of common acute osteomyelitis; however, EO has distinct imaging characteristics. Timely diagnosis, aggressive surgical debridement, and strong, targeted antibiotic therapy can result in favorable outcomes. Conversely, delayed diagnosis and treatment may lead to severe complications or death.

Objective To investigate serum metabolites and metabolic pathways alterations in patients with ischemic stroke(IS)through metabolomic analysis,and to identify reliable serum metabolic biomarkers for IS diagnosis.Methods This prospective study enrolled patients with IS admitted to the Department of Neurology at Xiangcheng People's Hospital of Suzhou from December 1,2022 to December 31,2023.Age-and sex-matched healthy individuals were recruited as controls during the same period.Baseline characteristics were collected,including age,sex,height,body mass index,and blood pressure.Venous blood samples were obtained after an 8 h fast for biochemical analysis of blood glucose,total bilirubin,serum creatinine,urea nitrogen,total cholesterol,triglycerides,high-density lipoprotein cholesterol,and low-density lipoprotein cholesterol.Serum metabolites of both groups were extracted and analyzed using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry.Metabolomic data were processed using Simca-p software for unsupervised principal component analysis(PCA)and orthogonal partial least squares discriminant analysis(OPLS-DA)to evaluate group separation and experimental stability.Differential metabolites were defined by variable importance in projection(VIP)≥1.0,fold change(FC)≥2.0 or ≤0.5,and P＜0.05.Drug-derived exogenous metabolites were excluded by cross-referencing the Human Metabolome Database(HMDB,https://hmdb.ca/)and PubChem(https://pubchem.ncbi.nlm.nih.gov/).MetaboAnalyst 6.0(http://www.metaboanalyst.ca),a comprehensive web-based tool for metabolomic data analysis,was employed to map differential metabolites to the Kyoto encyclopedia of genes and genomes(KEGG)databased and to perform pathway enrichment analysis.Machine learning models were developed using Python.Least absolute shrinkage and selection operator(LASSO)regression and random forest(RF)algorithms were employed to identify diagnostic biomarkers capable of effectively distinguishing IS patients from controls.Metabolites identified by both methods were integrated into an extreme gradient boosting(XGBoost)model.Model performance was evaluated using receiver operating characteristic(ROC)curves with 5-fold cross-validation and internal validation(70％training,30％validation set).Results A total of 51 IS patients and 51 matched controls were included.(1)A total of 1 255 serum metabolites were identified(964 in positive ion mode,291 in negative ion mode).PC A and OPLS-DA demonstrated distinct metabolic separation between IS patients and controls.In IS group,260 metabolites were upregulated and 337 downregulated in positive ion mode;99 were upregulated and 34downregulated in negative ion mode.(2)Among the 1 255 metabolites,259 were identified as differential metabolites based on the criteria of VIP ≥ 1.0,FC≥2.0 or≤0.5 and P＜0.05.After excluding drug-derived metabolite through referencing HMDB and PubChem databases,a total of 220 endogenous differential metabolites were found to coexist in both positive and negative ion modes.Among them,119 metabolites were up-regulated and 101 were down-regulated in the IS group.The expression of these 220 metabolites showed significant differences between the IS and control groups.(3)KEGG pathway analysis highlighted five dysregulated pathways:upregulation of denovo triacylglycerol biosynthesis,glycerophosphate shuttle,and cardiolipin biosynthesis;downregulation of bile acid biosynthesis and methylhistidine metabolism.(4)LASSO and RF algorithms identified 24 and 30 candidate biomarkers,respectively.Four overlapping metabolites were selected:2-((3R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z 971.571 29),arginine-conjugated cholic acid(m/z 587.379 21),laccaic acid A(m/z 576.010 93)and NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48).The expression levels of 2-((3R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z 971.571 29),arginine-conjugated cholic acid(m/z 587.379 21),and laccaic acid A(m/z 576.010 93)were upregulated,while the expression level of NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48)was downregulated.An IS diagnostic model was established based on four metabolic biomarkers using the XGBoost algorithm.The area under the ROC curve was 1.000(95％CI 1.000-1.000)in the training set and 0.988 in the validation set(95％CI 0.963-1.000).Conclusions Patients with IS exhibit significant metabolic disturbance.The four identified biomarkers may serve as potential biomarkers for the effective identification of IS:2-((3 R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z971.571 29),arginine-conjugated cholic acid(m/z587.379 21),laccaic acid A(m/z 576.010 93),and NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48).

Image 1.

A retrospective analysis was conducted on data from five patients comprehensively diagnosed with emphysematous osteomyelitis (EO) based on clinical, imaging, microbiological culture, and surgical findings at Hebei Medical University Third Hospital from December 2020 to May 2024. Among these five cases, there were three males and two females, aged between 11 and 69 years. Three patients had infection risk factors (two with diabetes and one with anemia), while two had no documented risk factors in their medical history. All patients presented with fever, localized pain at the infection site, and elevated inflammatory markers. Site of incidence: EO affected the lumbar spine in three cases, the ilium in one case, and the femur in one case. Pathogenic microorganisms: The causative agents included Klebsiella pneumoniae (two cases), Escherichia coli (one case), Burkholderia cepacia (one case), and a mixed infection of Staphylococcus aureus and Acinetobacter baumannii (one case). Imaging findings: Among the three patients who underwent X-ray examinations, one showed normal results, while two exhibited bone destruction. CT scans of all five cases revealed the characteristic "pumice sign" without periosteal reaction or osteosclerosis. MRI, performed on four patients, demonstrated bone destruction, swelling of surrounding soft tissues, and soft tissue abscess formation. Treatment and outcomes: Four patients underwent surgical debridement combined with perioperative antibiotic therapy. Of these, three recovered well, while one achieved good infection control but suffered severe joint destruction. One patient died after treatment was discontinued. The clinical and laboratory findings of EO resemble those of common acute osteomyelitis; however, EO has distinct imaging characteristics. Timely diagnosis, aggressive surgical debridement, and strong, targeted antibiotic therapy can result in favorable outcomes. Conversely, delayed diagnosis and treatment may lead to severe complications or death.

Objective To explore the value of ovarian-adnexal reporting and data system(O-RADS)MRI score combined with tumor markers(CA125+HE4)in ovarian tumors.Methods Data from 223 patients with ovarian tumors confirmed by pathology were analyzed retrospectively,including 260 lesions.The Kappa test was used to assess the consistency of O-RADS MRI score between low and high seniority physician groups.The receiver operating characteristic(ROC)curve was drawn to analyze the diagnostic effi-cacy of O-RADS MRI score combined with tumor markers(CA125+HE4)in ovarian tumors.Results The Kappa value of the O-RADS MRI score between low and high seniority physician groups was 0.803[95%confidence interval(CI)0.746-0.860].The sensitivity based on O-RADS MRI score for distinguishing benign and malignant ovarian tumors was 0.957 and 0.989,the specificity was 0.784 and 0.820,the accuracy was 0.846 and 0.881,the positive predictive value was 0.712 and 0.754,and the negative pre-dictive value was 0.970 and 0.993,and the area under the curve(AUC)was 0.871 and 0.905,respectively in the low and high senior-ity physician groups.Combined with tumor markers(CA125+HE4),the sensitivity and negative predictive value of both low and high seniority physician groups were 1.000.Conclusion The O-RADS MRI score has high diagnostic efficacy and good repeatability in ovarian tumors.Combined with tumor markers CA125 and HE4,the O-RADS MRI score can further improve the diagnostic sen-sitivity.

OBJECTIVE To investigate the distribution and drug resistance of multidrug-resistant bacteria in the neonatal intensive care unit of a three-A children's hospital in Henan Province,and to provide reference for ational drug use in clinical practice.METHODS Clinical specimens from hospitalized newborns in neonatal intensive care unit from a three-A children's hospital from Jan.1,2019 to Dec.31,2023 were subjected to etiological exam-ination and drug sensitivity test,and to analyze the distribution and drug resistance of multidrug-resistant bacteri-a in hospitalized newborns.RESULTS During the 5-year period,1139 strains of multidrug-resistant bacteria were i-solated,including 229 gram-positive bacteria(20.11％)and 910 gram-negative bacteria(79.89％).There were 92 strains of methicillin-resistant Staphylococcus aureus(MRSA)(accounting for 8.08％),57 strains(accounting for 5.00％)of methicillin-resistant coagulase-negative Staphylococcus epidermidis and 28 strains(accounting for 2.46％)of methicillin-resistant coagulase-negative human Staphylococcus.370 strains(accounting for 32.48)of carbapenem-resistant Klebsiella pneumoniae(CRKP),268 strains(accounting for 23.53％)of extenspectrum β-lactamase-producing Escherichia coli and 85 strains(accounting for 7.46％)of K.pneumoniae,there were 767 sputum specimens(67.34％),160 blood specimens from peripheral intravenous puncture and central venous cath-eterization(PICC)(14.05％),63 bronchoalveolar lavage fluid specimens(5.53％),29 secretion specimens(eye and wound secretions)(2.54％),and 120 other specimens(10.54％).K.pneumoniae and E.coli producing su-per-broad spectrum β-lactamase,CRKP and MRSA were the main drug-resistant bacteria.CONCLUSION The sit-uation of drug resistance in neonatal intensive care unit is serious,therefore monitoring bacterial resistance should be strengthened according to the clinical laboratory results,and antibiotics should be applied rationally.

Objective To investigate serum metabolites and metabolic pathways alterations in patients with ischemic stroke(IS)through metabolomic analysis,and to identify reliable serum metabolic biomarkers for IS diagnosis.Methods This prospective study enrolled patients with IS admitted to the Department of Neurology at Xiangcheng People's Hospital of Suzhou from December 1,2022 to December 31,2023.Age-and sex-matched healthy individuals were recruited as controls during the same period.Baseline characteristics were collected,including age,sex,height,body mass index,and blood pressure.Venous blood samples were obtained after an 8 h fast for biochemical analysis of blood glucose,total bilirubin,serum creatinine,urea nitrogen,total cholesterol,triglycerides,high-density lipoprotein cholesterol,and low-density lipoprotein cholesterol.Serum metabolites of both groups were extracted and analyzed using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry.Metabolomic data were processed using Simca-p software for unsupervised principal component analysis(PCA)and orthogonal partial least squares discriminant analysis(OPLS-DA)to evaluate group separation and experimental stability.Differential metabolites were defined by variable importance in projection(VIP)≥1.0,fold change(FC)≥2.0 or ≤0.5,and P＜0.05.Drug-derived exogenous metabolites were excluded by cross-referencing the Human Metabolome Database(HMDB,https://hmdb.ca/)and PubChem(https://pubchem.ncbi.nlm.nih.gov/).MetaboAnalyst 6.0(http://www.metaboanalyst.ca),a comprehensive web-based tool for metabolomic data analysis,was employed to map differential metabolites to the Kyoto encyclopedia of genes and genomes(KEGG)databased and to perform pathway enrichment analysis.Machine learning models were developed using Python.Least absolute shrinkage and selection operator(LASSO)regression and random forest(RF)algorithms were employed to identify diagnostic biomarkers capable of effectively distinguishing IS patients from controls.Metabolites identified by both methods were integrated into an extreme gradient boosting(XGBoost)model.Model performance was evaluated using receiver operating characteristic(ROC)curves with 5-fold cross-validation and internal validation(70％training,30％validation set).Results A total of 51 IS patients and 51 matched controls were included.(1)A total of 1 255 serum metabolites were identified(964 in positive ion mode,291 in negative ion mode).PC A and OPLS-DA demonstrated distinct metabolic separation between IS patients and controls.In IS group,260 metabolites were upregulated and 337 downregulated in positive ion mode;99 were upregulated and 34downregulated in negative ion mode.(2)Among the 1 255 metabolites,259 were identified as differential metabolites based on the criteria of VIP ≥ 1.0,FC≥2.0 or≤0.5 and P＜0.05.After excluding drug-derived metabolite through referencing HMDB and PubChem databases,a total of 220 endogenous differential metabolites were found to coexist in both positive and negative ion modes.Among them,119 metabolites were up-regulated and 101 were down-regulated in the IS group.The expression of these 220 metabolites showed significant differences between the IS and control groups.(3)KEGG pathway analysis highlighted five dysregulated pathways:upregulation of denovo triacylglycerol biosynthesis,glycerophosphate shuttle,and cardiolipin biosynthesis;downregulation of bile acid biosynthesis and methylhistidine metabolism.(4)LASSO and RF algorithms identified 24 and 30 candidate biomarkers,respectively.Four overlapping metabolites were selected:2-((3R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z 971.571 29),arginine-conjugated cholic acid(m/z 587.379 21),laccaic acid A(m/z 576.010 93)and NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48).The expression levels of 2-((3R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z 971.571 29),arginine-conjugated cholic acid(m/z 587.379 21),and laccaic acid A(m/z 576.010 93)were upregulated,while the expression level of NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48)was downregulated.An IS diagnostic model was established based on four metabolic biomarkers using the XGBoost algorithm.The area under the ROC curve was 1.000(95％CI 1.000-1.000)in the training set and 0.988 in the validation set(95％CI 0.963-1.000).Conclusions Patients with IS exhibit significant metabolic disturbance.The four identified biomarkers may serve as potential biomarkers for the effective identification of IS:2-((3 R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z971.571 29),arginine-conjugated cholic acid(m/z587.379 21),laccaic acid A(m/z 576.010 93),and NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48).

OBJECTIVE To investigate the distribution and drug resistance of multidrug-resistant bacteria in the neonatal intensive care unit of a three-A children's hospital in Henan Province,and to provide reference for ational drug use in clinical practice.METHODS Clinical specimens from hospitalized newborns in neonatal intensive care unit from a three-A children's hospital from Jan.1,2019 to Dec.31,2023 were subjected to etiological exam-ination and drug sensitivity test,and to analyze the distribution and drug resistance of multidrug-resistant bacteri-a in hospitalized newborns.RESULTS During the 5-year period,1139 strains of multidrug-resistant bacteria were i-solated,including 229 gram-positive bacteria(20.11％)and 910 gram-negative bacteria(79.89％).There were 92 strains of methicillin-resistant Staphylococcus aureus(MRSA)(accounting for 8.08％),57 strains(accounting for 5.00％)of methicillin-resistant coagulase-negative Staphylococcus epidermidis and 28 strains(accounting for 2.46％)of methicillin-resistant coagulase-negative human Staphylococcus.370 strains(accounting for 32.48)of carbapenem-resistant Klebsiella pneumoniae(CRKP),268 strains(accounting for 23.53％)of extenspectrum β-lactamase-producing Escherichia coli and 85 strains(accounting for 7.46％)of K.pneumoniae,there were 767 sputum specimens(67.34％),160 blood specimens from peripheral intravenous puncture and central venous cath-eterization(PICC)(14.05％),63 bronchoalveolar lavage fluid specimens(5.53％),29 secretion specimens(eye and wound secretions)(2.54％),and 120 other specimens(10.54％).K.pneumoniae and E.coli producing su-per-broad spectrum β-lactamase,CRKP and MRSA were the main drug-resistant bacteria.CONCLUSION The sit-uation of drug resistance in neonatal intensive care unit is serious,therefore monitoring bacterial resistance should be strengthened according to the clinical laboratory results,and antibiotics should be applied rationally.

Objective:To investigate the effects of different feeding formulas on the feeding and growth and metabolism of premature infants in the early postnatal period.Methods:Eligible premature infants with the gestational age of ≤ 34 weeks hospitalized from March 2023 to March 2024 were selected as per inclusion criteria, excluding those with congenital metabolic diseases, severe congenital heart disease and developmental malformations of digestive tract. According to the feeding formulas within 2 weeks after birth, premature infants were divided into three groups, namely donor human milk (DHM) group, preterm formula (PF) group and extensively hydrolyzed formula (eHF) group. The characteristics of premature infants, perinatal condition, feeding formulas, milk intake on the 7th and 14th day, the time to the daily milk intake of 120ml/kg and 150ml/kg respectively, the time on parenteral nutrition, the length of hospitalization, feeding intolerance, cholestasis, extrauterine growth retardation and biochemical metabolic indexes at 7 days, 14 days and discharge were collected. The differences of feeding and biochemical metabolic parameters were compared across the three groups.Results:A total of 108 cases were enrolled ,of whom 39 were in DHM group, 37 in PF group and 32 in eHF group. There was no significant difference in gestational age, birth weight, head circumference and maternal complications across the three groups. The milk intake in the DHM group was (50.7±29.1) ml/(kg·d) on the 7th day, compared with (34.2±27.3) ml/(kg·d) in PF group ( P=0.031), and (103.1±36.7) ml/(kg·d) on the 14th day, compared with (73.9±39.2) ml/(kg·d) in the PF group. Compared with the PF group, the DHM group reached the daily milk intake of 120 ml/(kg·d) earlier [(18.5±10.4) days vs. (24.1±10.3) days, P=0.020], had shorter duration of parenteral nutrition [(17.9±10.9) days vs. (23.2±11.2) days, P=0.042], and lower incidence of feeding intolerance (28.2% vs. 48.6%). The length of hospitalization in DHM group was shorter than that in PF group [(33.8±15.5) days vs. (37.8±17.6) days], but there was no significant difference ( P>0.05). There was no significant difference between the DHM group and the eHF group in terms of the milk intake on the 7th and 14th day, the time to the daily milk intake of 120 ml/(kg·d), the time on parenteral nutrition, the length of hospitalization and feeding intolerance. At 1 and 2 weeks after birth, alkaline phosphatase in DHM group was higher than that in PF group and eHF group ( P<0.05), but there was no significant difference in biochemical nutritional metabolism parameters (hemoglobin, urea nitrogen, albumin, prealbumin, alkaline phosphatase and total bile acid) across the three groups at discharge( P>0.05). Conclusion:Early use of DHM in premature infants is better tolerated than PF and can help achieve complete enteral nutrition earlier and shorten the use of parenteral nutrition, while not affecting the growth and development of premature infants.