PURPOSE: The rate of complications among patients undergoing surgery has increased due to infection with SARS-CoV-2 and other variants of concern. However, Omicron has shown decreased pathogenicity, raising questions about the risk of postoperative complications among patients who are infected with this variant. This study aimed to investigate complications and related factors among patients with recent Omicron infection prior to undergoing orthopedic surgery. METHODS: A historical control study was conducted. Data were collected from all patients who underwent surgery during 2 distinct periods: (1) between Dec 12, 2022 and Jan 31, 2023 (COVID-19 positive group), (2) between Dec 12, 2021 and Jan 31, 2022 (COVID-19 negative control group). The patients were at least 18 years old. Patients who received conservative treatment after admission or had high-risk diseases or special circumstances (use of anticoagulants before surgery) were excluded from the study. The study outcomes were the total complication rate and related factors. Binary logistic regression analysis was used to identify related factors, and odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the impact of COVID-19 infection on complications. RESULTS: In the analysis, a total of 847 patients who underwent surgery were included, with 275 of these patients testing positive for COVID-19 and 572 testing negative. The COVID-19-positive group had a significantly higher rate of total complications (11.27%) than the control group (4.90%, p < 0.001). After adjusting for relevant factors, the OR was 3.08 (95% CI: 1.45-6.53). Patients who were diagnosed with COVID-19 at 3-4 weeks (OR = 0.20 (95% CI: 0.06-0.59), p = 0.005), 5-6 weeks (OR = 0.16 (95% CI: 0.04-0.59), p = 0.010), or ≥7 weeks (OR = 0.26 (95% CI: 0.06-1.02), p = 0.069) prior to surgery had a lower risk of complications than those who were diagnosed at 0-2 weeks prior to surgery. Seven factors (age, indications for surgery, time of operation, time of COVID-19 diagnosis prior to surgery, C-reactive protein levels, alanine transaminase levels, and aspartate aminotransferase levels) were found to be associated with complications; thus, these factors were used to create a nomogram. CONCLUSION Omicron continues to be a significant factor in the incidence of postoperative complications among patients undergoing orthopedic surgery. By identifying the factors associated with these complications, we can determine the optimal surgical timing, provide more accurate prognostic information, and offer appropriate consultation for orthopedic surgery patients who have been infected with Omicron.
Humans ; COVID-19/complications* ; Male ; Female ; Middle Aged ; Postoperative Complications/epidemiology* ; SARS-CoV-2 ; Orthopedic Procedures/adverse effects* ; Aged ; Nomograms ; Adult ; Retrospective Studies ; Risk Factors

OBJECTIVE: To evaluate the effect and safety of Chinese medicine (CM) Fuzheng Huazhuo Decoction (FHD) in treating patients with coronavirus disease 2019 (COVID-19) who persistently tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: This retrospective cohort study was conducted at Shanghai New International Expo Center shelter hospital in China between April 1 and May 30, 2022. Patients diagnosed as COVID-19 with persistently positive SARS-CoV-2 reverse transcription-polymerase chain reaction (RT-PCR) test results for ⩾8 days after diagnosis were enrolled. Patients in the control group received conventional Western medicine (WM) treatment, while those in the FHD group received conventional WM plus FHD for at least 3 days. The primary outcome was viral clearance time. Secondary outcomes included negative conversion rate within 14 days, length of hospital stay, cycle threshold (Ct) values of the open reading frame 1ab (ORF1ab) and nucleocapsid protein (N) genes, and incidence of new-onset symptoms during hospitalization. Adverse events (AEs) that occurred during the study period were recorded. RESULTS: A total of 1,765 eligible patients were enrolled in this study (546 in the FHD group and 1,219 in the control group). Compared with the control group, patients receiving FHD treatment showed shorter viral clearance time for nucleic acids [hazard ratio (HR): 1.500, 95% confidence interval (CI): 1.353-1.664, P<0.001] and hospital stays (HR: 1.371, 95% CI: 1.238-1.519, P<0.001), and a higher negative conversion rate within 14 days (96.2% vs. 82.6%, P<0.001). The incidence of new-onset symptoms was 59.5% in the FHD group, similar to 57.8% in the control group (P>0.05). The Ct values of ORF1ab and N genes increased more rapidly over time in the FHD group than those in the control group post-randomization (ORF1ab gene: β =0.436±0.053, P<0.001; N gene: β =0.415 ±0.053, P<0.001). The incidence of AEs in the FHD group was lower than that in the control group (24.2% vs. 35.4%, P<0.001). No serious AEs were observed. CONCLUSION FHD was effective and safe for patients with persistently positive SARS-CoV-2 PCR tests. (Registration No. ChiCTR2200063956).
Humans ; Drugs, Chinese Herbal/adverse effects* ; Retrospective Studies ; Male ; Female ; Middle Aged ; COVID-19 Drug Treatment ; SARS-CoV-2/drug effects* ; COVID-19/virology* ; Adult ; Aged ; Treatment Outcome

mRNA therapy is an emerging treatment that has become a frontier and hot topic in the field of biomedicine.To explore the trend in the development of mRNA therapy,this paper conducts an analysis from the perspectives of papers and patents,examining multiple dimensions including development trend,research areas,and high-value research.The study reveals the following findings:Global research in mRNA therapy is growing rapidly.Basic research mainly focuses on oncology,chemistry-multidisciplinary,biochemistry and molecular biology,while applied research centers on mRNA concerning genetic engineering,isolation,synthesis,purification,and the development of medicines.High-value research mainly centers on topics such as mRNA delivery,composition,manufacture,modification,and the development of various mRNA-based therapies.

Aim To explore the mechanism of Lycium barbarum glycopeptide(LbGP)improving aging in rat primary ovarian granulosa cells.Methods This study divided the cells into a normal group,a DOX group,and four different LbGP concentration treatment groups post-DOX intervention.Results Cell proliferation was assessed using CCK-8,EDU,and Ki67 assays,while aging markers and mitochondrial function-related fac-tors were detected using immunofluorescence and West-ern blotting.The results showed that,compared to the DOX group,LbGP treatment significantly increased cell viability(P＜0.05)and promoted proliferation(P＜0.05).Post LbGP treatment,the β-galactosidase-posi-tive area in cells was significantly reduced compared to the DOX group(P＜0.05).Immunofluorescence re-sults indicated that,compared to the DOX group,levels of p21 and γH2AX significantly decreased(P＜0.05),while pRB increased(P＜0.05)after LbGP treatment.Western blot results showed that,compared to the DOX group,the aging phenotype proteins p21 and p53 significantly decreased(P＜0.05),and pRB notably increased(P＜0.05)in the LbGP treatment group.The release of cytC into the cytoplasm and the activated caspase-9 significantly decreased(P＜0.05);levels of CAMKK2,pAMPK,and mitochondrial calcium homeostasis regulator MCU increased(P＜0.05);nuclear energy metabolism-related proteins SirT1,PGC1α/β and ATP5A1 significantly increased(P＜0.05);compared to the DOX group,ROS levels significantly decreased after LbGP treatment(P＜0.05).Conclusions The results suggest that LbGP can ameliorate DOX-induced aging in rat primary ovar-ian granulosa cells,potentially through the upregulation of the CAMKKβ/AMPK signaling pathway,thereby im-proving mitochondrial calcium homeostasis and increas-ing the expression levels of cell energy metabolism-re-lated regulatory proteins.This provides an experimen-tal basis for LbGP's potential role in supporting the im-provement of ovarian function.

BACKGROUND:Kaempferol has anti-osteoporotic effects,but the mechanisms by which kaempferol regulates gut microbiota and metabolites to prevent and treat osteoporosis remain unclear.OBJECTIVE:To exploring the potential mechanisms by which kaempferol inhibit osteoporosis based on gut microbiota and comprehensive targeted metabolomics.METHODS:Eighteen female Sprague-Dawley rats were randomly divided into three groups:sham operation group,model group,and kaempferol group,with 6 rats in each group.Animal models of osteoporosis were made in the latter two groups through removal of bilateral ovaries.Eight weeks after modeling,the sham operation and model groups were gavaged with distilled water,and the kaempferol group was gavaged with 40 mg/kg kaempferol.Continuous administration in each group was carried out for 12 weeks.Rat fecal samples were collected for 16S rDNA amplicon sequencing to observe changes in the gut microbiota structure.Serum samples were subjected to comprehensive targeted metabolomics analysis using ultra-high performance liquid chromatography-tandem mass spectrometry technology,along with a proprietary database and multivariate statistical analysis.RESULTS AND CONCLUSION:After 12 weeks of continuous intervention,the results of 16S rDNA amplicon sequencing showed that compared with the sham operation group,the abundance of gut microbiota increased in the model group.Compared with the model group,kaempferol group exhibited a statistically significant increase in the abundance of the genus Latilactobacillus(P=0.021),while the abundances of Pantoea(P=0.034),Enterorhabdus(P=0.000),Monoglobus(P=0.024),Butyricimonas(P=0.034),Rothia(P=0.043),and Clostridia(P=0.004)were significantly downregulated.After 12 weeks of continuous intervention,the results of the serum samples analyzed by broad-targeted metabolomics revealed that 120 and 79 metabolites were identified between the sham operation and model groups and between the model and kaempferol groups,respectively.Among the three groups,there were 17 overlapping differentially expressed metabolites,including Cis-aconitic acid,barbituric acid,L-homocitrulline,3,4,5-trimethoxycinnamic acid,L-3-phenyllactic acid,cyclo(pro-pro),L-phenylalanine-L-serine,proline-isoleucine,L-donoraminoacetic acid-L-phenylalanineacetic acid,and phenylalanine-aspartic acid.Most of them belong to amino acids and their metabolites,glycerophospholipids and fatty acyls.The Kyoto Encyclopedia of Genes and Genomes pathways involved in the differential metabolites were mainly enriched in D-amino acid metabolism,histidine metabolism,propionate metabolism,lysine degradation,fatty acid metabolism and sphingolipid metabolism.After 12 weeks of continuous intervention,combined analysis revealed that genera such as Enterorhabdus,Latilactobacillus,Rothia,and Ruminococcus were closely associated with differential serum metabolites.To conclude,kaempferol may exert its anti-osteoporotic effects by modulating the abundance,diversity,and structure of gut microbiota,thereby regulating the metabolism of amino acids,their metabolites,and fatty acids.

Objective To elucidate the mechanism of Polygonatum kingianum water extract(PW)on the proliferation and colonization of Lactobacillus reuteri 1.2838,the differential expression of genes associated with proliferation,the quorum sensing signal molecule autoinducer-2(AI-2),and stress resistance were detected.Method L.reuteri 1.2838 was anaerobically cultured at 37℃in MRS medium supplemented with 0.0126 g·mL-1 PW,and the growth curve was subsequently plotted.The quantification of AI-2 production was conducted using the bioluminescence assay with Vibrio harveyi BB170.Transcriptome sequencing was executed using Illumina HiSeq technology,followed by the identification of differentially expressed genes.The expression profiles of these genes were analyzed,and real-time quantitative PCR was employed for validation.Results Incubation with PW resulted in increased proliferation and AI-2 production capacity of L.reuteri 1.2838.Transcriptome sequencing revealed 425 genes with significant differential expression,comprising 253 upregulated and 172 downregulated genes.Post GO and KEGG annotation analysis,genes related to L.reuteri 1.2838 proliferation,including pdhA,pshB,dlat,dld,genes pertinent to AI-2 production such as luxS,sec,and genes linked to the strain's stress resistance,groEL,groES,gltC,exhibited an upregulated expression pattern.Conclusion PW facilitates the proliferation and colonization of L.reuteri 1.2838 by influencing the tricarboxylic acid cycle,quorum sensing,and the strain's stress resistance,thus offering theoretical support for the development of both Polygonatum kingianum and Lactobacillus reuteri.

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.