OBJECTIVES: Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia. METHODS: Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases. RESULTS: A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. CONCLUSIONS AEBP1 and COL8A2 have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.
Sarcopenia/genetics* ; Osteoarthritis/genetics* ; Computational Biology/methods* ; Humans ; Protein Interaction Maps/genetics* ; Animals ; Mice ; Gene Expression Profiling ; Gene Ontology ; Transcriptome ; Male ; Signal Transduction/genetics* ; Gene Regulatory Networks

OBJECTIVES: Osteoarthritis (OA) is one of the most common chronic degenerative diseases, with chondrocyte apoptosis and extracellular matrix (ECM) degradation as the major pathological changes. The mechanical stimulation can attenuate chondrocyte apoptosis and promote ECM synthesis, but the underlying molecular mechanisms remain unclear. This study aims to investigate the role of primary cilia (PC) in mediating the effects of mechanical stimulation on OA progression. METHODS: In vivo, conditional knockout mice lacking intraflagellar transport 88 (IFT88^flox/flox IFT88 knockout; i.e., primary cilia-deficient mice) were generated, with wild-type mice as controls. OA models were established via anterior cruciate ligament transection combined with destabilization of the medial meniscus, followed by treadmill exercise intervention. OA progression was evaluated by hematoxylin-eosin staining, safranin O-fast green staining, and immunohistochemistry; apoptosis was assessed by TUNEL staining; and limb function by rotarod testing. In vitro, primary articular chondrocytes were isolated from mice and transfected with lentiviral vectors to suppress IFT88 expression, thereby constructing a primary cilia-deficient cell model. Interleukin-1β (IL-1β) was used to induce an inflammatory environment, while cyclic tensile strain (CTS) was applied via a cell stretcher to mimic mechanical loading on chondrocytes. Immunofluorescence and Western blotting were used to detect the protein expression levels of type II collagen α1 chain (COL2A1), primary cilia, IFT88, and caspase-12; reverse transcription polymerase chain reaction was performed to assess COL2A1 mRNA levels; and flow cytometry was used to evaluate apoptosis. RESULTS: In vivo, treadmill exercise significantly reduced Osteoarthritis Research Society International (OARSI) scores and apoptotic cell rates, and improved balance ability in wild-type OA mice, whereas IFT88-deficient OA mice showed no significant improvement. In vitro, CTS inhibited IL-1β-induced ECM degradation and apoptosis in primary chondrocytes; however, this protective effect was abolished in cells with suppressed primary cilia expression. CONCLUSIONS Mechanical stimulation delays OA progression by mediating signal transduction through primary cilia, thereby inhibiting cartilage degeneration and chondrocyte apoptosis.
Animals ; Chondrocytes/cytology* ; Apoptosis/physiology* ; Mice ; Cilia/metabolism* ; Osteoarthritis/pathology* ; Extracellular Matrix/metabolism* ; Mice, Knockout ; Disease Progression ; Interleukin-1beta ; Male ; Cells, Cultured

Objective:To analyze the efficacy of laparoscopic radical surgery, compared to open surgery, for incidental gallbladder cancer following cholecystectomy.Methods:Clinical data of 106 patients with incidental gallbladder cancer treated at the Second Affiliated Hospital Zhejiang University School of Medicine from April 2010 to February 2018 were retrospectively analyzed, including 66 males and 40 females, aged (64.7±7.9) years old. According to surgical approach, patients were divided into the laparoscopic group ( n=45) and open group ( n=61). Perioperative data, including intraoperative blood loss, postoperative hospital stay, and postoperative complications, were compared between the groups. Follow-ups were conducted via outpatient visits or telephone reviews. Survival analysis was performed using the Kaplan-Meier method, and the survival rates were compared using the log-rank test. Results:All 45 patients in the laparoscopic group successfully underwent the surgery without conversion to open surgery. Compared to the open group, the laparoscopic group had a reduced intraoperative blood loss [(100±25) ml vs. (200±46) ml] and a shortened postoperative hospital stay [3(2, 5) d vs. 5(4, 7) d] (both P<0.05). The postoperative complication rates were 6.7% (3/45) in the laparoscopic group and 13.1% (8/61) in the open group ( χ2=4.16, P=0.041). The cumulative survival rate after radical surgery for incidental gallbladder cancer was better in the laparoscopic group ( χ2=4.58, P=0.032). Conclusion:Compared to open surgery, laparoscopic radical surgery for incidental gallbladder cancer showed benefits in intraoperative blood loss, postoperative hospital stay, complication rates, and cumulative survival.

The Omicron family of SARS-CoV-2 variants are currently driving the COVID-19 pandemic. Here we analyzed the clinical laboratory test results of 9911 Omicron BA.2.2 sublineages-infected symptomatic patients without earlier infection histories during a SARS-CoV-2 outbreak in Shanghai in spring 2022. Compared to an earlier patient cohort infected by SARS-CoV-2 prototype strains in 2020, BA.2.2 infection led to distinct fluctuations of pathophysiological markers in the peripheral blood. In particular, severe/critical cases of COVID-19 post BA.2.2 infection were associated with less pro-inflammatory macrophage activation and stronger interferon alpha response in the bronchoalveolar microenvironment. Importantly, the abnormal biomarkers were significantly subdued in individuals who had been immunized by 2 or 3 doses of SARS-CoV-2 prototype-inactivated vaccines, supporting the estimation of an overall 96.02% of protection rate against severe/critical disease in the 4854 cases in our BA.2.2 patient cohort with traceable vaccination records. Furthermore, even though age was a critical risk factor of the severity of COVID-19 post BA.2.2 infection, vaccination-elicited protection against severe/critical COVID-19 reached 90.15% in patients aged ≽ 60 years old. Together, our study delineates the pathophysiological features of Omicron BA.2.2 sublineages and demonstrates significant protection conferred by prior prototype-based inactivated vaccines.
Humans ; Aged ; Middle Aged ; COVID-19/prevention & control* ; SARS-CoV-2 ; Pandemics/prevention & control* ; China/epidemiology* ; Disease Outbreaks/prevention & control* ; Vaccination

Pathogenic microbes can induce cellular dysfunction, immune response, and cause infectious disease and other diseases including cancers. However, the cellular distributions of pathogens and their impact on host cells remain rarely explored due to the limited methods. Taking advantage of single-cell RNA-sequencing (scRNA-seq) analysis, we can assess the transcriptomic features at the single-cell level. Still, the tools used to interpret pathogens (such as viruses, bacteria, and fungi) at the single-cell level remain to be explored. Here, we introduced PathogenTrack, a python-based computational pipeline that uses unmapped scRNA-seq data to identify intracellular pathogens at the single-cell level. In addition, we established an R package named Yeskit to import, integrate, analyze, and interpret pathogen abundance and transcriptomic features in host cells. Robustness of these tools has been tested on various real and simulated scRNA-seq datasets. PathogenTrack is competitive to the state-of-the-art tools such as Viral-Track, and the first tools for identifying bacteria at the single-cell level. Using the raw data of bronchoalveolar lavage fluid samples (BALF) from COVID-19 patients in the SRA database, we found the SARS-CoV-2 virus exists in multiple cell types including epithelial cells and macrophages. SARS-CoV-2-positive neutrophils showed increased expression of genes related to type I interferon pathway and antigen presenting module. Additionally, we observed the Haemophilus parahaemolyticus in some macrophage and epithelial cells, indicating a co-infection of the bacterium in some severe cases of COVID-19. The PathogenTrack pipeline and the Yeskit package are publicly available at GitHub.
COVID-19 ; Humans ; RNA ; SARS-CoV-2/genetics* ; Single-Cell Analysis/methods* ; Transcriptome

Emerging evidence indicates that the gut microbiome contributes to the host immune response to infectious diseases. Here, to explore the role of the gut microbiome in the host immune responses in COVID-19, we conducted shotgun metagenomic sequencing and immune profiling of 14 severe/critical and 24 mild/moderate COVID-19 cases as well as 31 healthy control samples. We found that the diversity of the gut microbiome was reduced in severe/critical COVID-19 cases compared to mild/moderate ones. We identified the abundance of some gut microbes altered post-SARS-CoV-2 infection and related to disease severity, such as Enterococcus faecium, Coprococcus comes, Roseburia intestinalis, Akkermansia muciniphila, Bacteroides cellulosilyticus and Blautia obeum. We further analyzed the correlation between the abundance of gut microbes and host responses, and obtained a correlation map between clinical features of COVID-19 and 16 severity-related gut microbe, including Coprococcus comes that was positively correlated with CD3⁺/CD4⁺/CD8⁺ lymphocyte counts. In addition, an integrative analysis of gut microbiome and the transcriptome of peripheral blood mononuclear cells (PBMCs) showed that genes related to viral transcription and apoptosis were up-regulated in Coprococcus comes low samples. Moreover, a number of metabolic pathways in gut microbes were also found to be differentially enriched in severe/critical or mild/moderate COVID-19 cases, including the superpathways of polyamine biosynthesis II and sulfur oxidation that were suppressed in severe/critical COVID-19. Together, our study highlighted a potential regulatory role of severity related gut microbes in the immune response of host.
COVID-19 ; Clostridiales ; Gastrointestinal Microbiome ; Humans ; Immunity ; Leukocytes, Mononuclear ; SARS-CoV-2

Objective:To explore the postprandial plasma low-density lipoprotein cholesterol (LDL-C) changes by various detection methods.Methods:A total of 85 subjects admitted to the Second Xiangya Hospital of Central South University from November 2017 to May 2019 were included. Serum samples were collected from fasting and the 2 nd hour and the 4 th hour after breakfast. Serum lipid levels were measured with enzymatic assays and nuclear magnetic resonance spectroscopy (NMRS), and proprotein invertase subtilisin/kexin type 9 (PCSK9) levels were measured with enzyme-linked immunosorbent assays. The differences of blood lipid components at different time points were compared by Friedman two-way rank analysis of variance and Wilcoxon signed rank test, and the correlation between PCSK9 level and lipoprotein particles was analyzed by Spearman correlation. Results:Measured by enzymatic assays, compared with the fasting state, LDL-C decreased at the 2 nd hour and the 4 th hour after the meal (2.58[2.09, 3.12], 2.47[1.92, 3.02], 2.37[1.82, 2.80] mmol/L, P<0.001). Measured by NMRS, the concentration of LDL particles (1 086[830, 1 239], 1 083[848, 1 213], 1 061[814, 1 213] nmol/L, P=0.417) did not change significantly, and cholesterol in LDL particles were 2.13 (1.56, 2.54), 2.16 (1.68, 2.50), 2.06 (1.58, 2.50) mmol/L, respectively ( P=0.047)，and postprandial cholesterol in LDL particles in the 2 nd hour and in the 4 th hour did not change significantly compared with fasting ( P>0.05). while the concentration of large LDL particles (185.2[150.6,221.6], 173.0[144.8,220.3], 178.1[144.0,233.6] nmol/L, P=0.001), and the cholesterol level in large LDL particles (0.49[0.39, 0.57], 0.47[0.38, 0.57], 0.46[0.37, 0.58]mmol/L, P<0.001) decreased after the meal. The PCSK9 level also decreased significantly after the meal (299[233, 397], 257[208, 342], 251[215, 340] ng/ml, P<0.001). There was an independent positive correlation between the decrease of PCSK9 levels and the increase of remnant cholesterol detected by MNRS after the meal ( r=0.232, P=0.035). Conclusions:The postprandial LDL-C level measured by NMRS and enzymatic assays is not consistent. The decrease of LDL-C measured by enzymatic assays is not caused by the clearance of LDL particles, but by the redistribution of cholesterol in each LDL subfraction.

Objective:To investigate the computed tomography (CT) features of adenocarcinoma of esophagogastric junction (AEG) after neoadjuvant chemotherapy.Methods:The retrospective and descriptive study was conducted. The clinicopathological data of 59 patients with AEG who underwent neoadjuvant chemotherapy in Peking University Cancer Hospital from February 2010 to November 2014 were collected. There were 51 males and 8 females, aged from 46 to 82 years, with a median age of 63 years. All the 59 patients underwent enhanced CT examination before and after neoadjuvant chemotherapy. Observation indicators: (1) pathological examination and neoadjuvant chemotherapy of patients with AEG; (2) results of CT examination in patients with AEG, including ① qualitative indicators of CT and ② quantitative indicators of CT. Measurement data with skewed distribution were represented as M( P25, P75) or M (range), and comparison between groups was analyzed using the Mann-Whitney U test. Count data were described as absolute numbers, and comparison between groups was analyzed by the chi-square test. Results:(1) Pathological examination and neoadjuvant chemotherapy of patients with AEG: of the 59 patients with AEG, high-differentiated adenocarcinoma was observed in 1 patient, moderate-differentiated adenocarcinoma in 40 patients, and low-differentiated adenocarcinoma in 18 patients. Effective response to neoadjuvant chemotherapy was observed in 13 patients, including 6 patients of pathological tumor regression grading (pTRG) 0 and 7 of pTRG 1; poor response was observed in 46 patients, including 12 patients of pTRG 2 and 34 patients of pTRG 3. (2) Results of CT examination in patients with AEG. ① Qualitative indicators of CT: for the 13 patients with effective response to neoadjuvant chemotherapy, 13 had the presence of ulcers, 5 had layered enhancement, 10 had infiltration of adventitia surface, and 2 had positive extramural venous invasion (EMVI) before neoadjuvant chemotherapy; after neoadjuvant chemotherapy, 13 had shallowed or disappeared ulcers, 7 patients had changed enhancement pattern, 3 had infiltration of adventitia surface, and 1 had positive EMVI. For the 46 patients with poor response to neoadjuvant chemotherapy, 28 had the presence of ulcers, 18 had layered enhancement, 37 had infiltration of adventitia surface, and 22 had positive EMVI before neoadjuvant chemotherapy; after neoadjuvant chemotherapy, 23 had shallowed or disappeared ulcers, 7 patients had changed layered enhancement pattern, 33 had infiltration of adventitia surface and 21 had positive EMVI, respectively. There was no significant difference in the layered enhancement or infiltration of adventitia surface before neoadjuvant chemotherapy between patients with different treatment response ( χ2=0.002, 0.000, P>0.05). There were significant differences in the presence of ulcers and positive EMVI before neoadjuvant chemotherapy between patients with different treatment response ( χ2=5.591, 4.421, P<0.05). After neoadjuvant chemotherapy, there were significant differences in the changes of layered enhancement pattern, infiltration of adventitia surface and positive EMVI between patients with different treatment response ( χ2=6.359, 10.090, 4.728, P<0.05); while there was no significant difference in the shallowed or disappeared ulcers between patients with different treatment response ( χ2=1.239, P>0.05). ② Quantitative indicators of CT: for the 13 patients with good response to neoadjuvant chemotherapy, the maximum tumor height, the maximum tumor area, enhanced CT value of the lesion before neoadjuvant chemotherapy were 1.37 cm(0.94 cm, 1.88 cm), 8.9 cm 2 (4.7 cm 2, 9.9 cm 2), 53 HU(47 HU, 63 HU), respectively. After neoadjuvant chemotherapy, the above indicators were 1.17 cm(0.79 cm, 1.29 cm), 4.4 cm 2(2.5 cm 2, 6.1 cm 2), 30 HU(25 HU, 53 HU), respectively. The change rates of the maximum tumor height, the maximum tumor area, and enhanced CT value of the lesion were -23%(-42%, 9%), -51%(-60 %, -21%), -44%(-51%, 19%), respectively. For the 46 patients with poor response to neoadjuvant chemotherapy, the maximum tumor height, the maximum tumor area, enhanced CT value of the lesion were 1.57 cm(1.21 cm, 1.96 cm), 9.4 cm 2(6.6 cm 2, 13.1 cm 2), 60 HU(53 HU, 66 HU) before neoadjuvant chemotherapy, respectively. After neoadjuvant chemotherapy, the above indicators were 1.16 cm(0.94 cm, 1.37 cm), 6.2 cm 2(4.8 cm 2, 8.1 cm 2), 55 HU(47 HU, 65 HU), respectively. The change rates of the maximum tumor height, the maximum tumor area, and enhanced CT value of the lesion were -27%(-38%, -9%), -33%(-47%, -12%), -9%(-22%, 9%), respectively. There was no significant difference in the maximum tumor height, the maximum tumor area, enhanced CT value of the lesion before neoadjuvant chemotherapy between patients with different treatment response ( Z=-1.372, -1.372, -1.331, P>0.05). There was no significant difference in the maximum tumor height after neoadjuvant chemotherapy between patients with different treatment response ( Z=-0.503, P>0.05), while there were significant differences in the maximum tumor area and CT value of the lesion ( Z=-2.743, -3.049, P<0.05). There was no significant difference in the change rate of the maximum tumor height or the maximum tumor area between patients with different treatment response ( Z=0.000, -1.481, P>0.05), while there was a significant difference in the change rate of CT value of the lesion ( Z=-3.231, P<0.05). Conclusion:Effective response of AEG to neoadjuvant chemotherapy was characterized by the changes in tumor layered enhancement pattern, reduction in the maximum tumor area, reduced CT value of the lesion, negative infiltration of adventitia surface, and negative EMVI.

The ongoing pandemic of Coronavirus disease 19 (COVID-19) is caused by a newly discovered β Coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). How long the adaptive immunity triggered by SARS-CoV-2 can last is of critical clinical relevance in assessing the probability of second infection and efficacy of vaccination. Here we examined, using ELISA, the IgG antibodies in serum specimens collected from 17 COVID-19 patients at 6-7 months after diagnosis and the results were compared to those from cases investigated 2 weeks to 2 months post-infection. All samples were positive for IgGs against the S- and N-proteins of SARS-CoV-2. Notably, 14 samples available at 6-7 months post-infection all showed significant neutralizing activities in a pseudovirus assay, with no difference in blocking the cell-entry of the 614D and 614G variants of SARS-CoV-2. Furthermore, in 10 blood samples from cases at 6-7 months post-infection used for memory T-cell tests, we found that interferon γ-producing CD4
Adaptive Immunity/physiology* ; Adult ; Aged ; Antibodies, Neutralizing/blood* ; COVID-19/immunology* ; Cohort Studies ; Female ; Humans ; Immunoglobulin G/blood* ; Male ; Middle Aged ; SARS-CoV-2/immunology* ; T-Lymphocytes/physiology* ; Time Factors ; Viral Proteins/immunology*

Objective To evaluate the value of multi?slice CT?based tumor predominant feeding artery sign in the localization diagnosis of exophytic tumors in the pancreaticogastric space. Methods CT images of 34 patients with pathologically proven exophytic tumors located in the pancreaticogastric space including 20 gastric gastrointestinal stromal tumors (GIST) and 14 pancreatic tumors, 7 patients of neuroendocrine neoplasms (NEN) and 7 patients of solid pseudopapillary neoplasms (SPN) were retrospectively analyzed. Two radiologists identified the tumor feeding arteries of the tumors and made the localization diagnoses. The inter?observer agreement was evaluated by Kappa coefficient. Chi?square test or Fisher exact test was used to compare the visualization of tumor predominant feeding artery sign in the two groups. Results The tumor feeding arteries were identified in 19 of 20 gastric GISTs and 13 of 14 pancreatic tumors. The two observers had a good agreement on the origins of the tumor feeding arteries (Kappa coefficient: 0.681). There was statistically significant difference in the origins of the tumor feeding arteries between the two groups (χ2=23.86,P<0.01). The blood supplies of most GISTs originated from gastric arteries, while those of most pancreatic tumors originated from the pancreatic branch of splenic artery. The tumor predominant feeding artery sign was identified in 17 gastric GISTs (17/20, 85.0%) and 11 pancreatic tumors (11/14, 78.6%). There was no statistically significant difference in the positive rate of the sign between the two groups (P=1.000). For all tumors enrolled, the sensitivities, specificities, accuracies, positive predictive values, and negative predictive values of the sign for the localization diagnosis of gastric GISTs and pancreatic tumors were 85.0% (17/20), 92.9% (13/14), 88.2% (30/34), 94.4% (17/18), 81.3% (13/16) and 71.4% (10/14), 100.0% (20/20), 88.2% (30/34), 100.0% (10/10), 83.3% (20/24), respectively. Conclusion The tumor predominant feeding artery sign on multi?slice CT can assist in the localization diagnosis of gastric and pancreatic exophytic tumors in the pancreaticogastric space.