Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment. Oral squamous cell carcinoma (OSCC), a representative hypoxic tumor, has a heterogeneous internal metabolic environment. To clarify the relationship between different metabolic regions and the tumor immune microenvironment (TME) in OSCC, Single cell (SC) and spatial transcriptomics (ST) sequencing of OSCC tissues were performed. The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data. The metabolic activity of each spot was calculated using scMetabolism, and k-means clustering was used to classify all spots into hyper-, normal-, or hypometabolic regions. CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others. Through CellPhoneDB and NicheNet cell-cell communication analysis, it was found that in the hypermetabolic region, fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts (iCAFs), and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12. The secretion of CXCL12 recruits regulatory T cells (Tregs), leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment. This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC, ST and TCGA bulk data, and highlights potential targets for therapy.
Humans ; Carcinoma, Squamous Cell/metabolism* ; Squamous Cell Carcinoma of Head and Neck ; Mouth Neoplasms/metabolism* ; Immunosuppression Therapy ; Transforming Growth Factor beta ; Head and Neck Neoplasms ; Gene Expression Profiling ; Tumor Microenvironment

Objective To analyze the pathogenic characteristics and drug sensitivity of candidaemia,and construct a short-term mortality risk prediction scoring model.Methods The clinical data of patients with candidaemia admitted to the 909 Hospital of Joint Logistics Support Force from January 2011 to December 2020 were retrospectively analyzed,and the composition of pathogen composition,drug sensitivity test results and incidence of hospitalized patients were analyzed.324 cases of candidaemia were randomly divided into modeling group(190 cases)and validation group(134 cases),and the risk factors were screened by binary logistic regression.According to the odds ratio(OR)score,the 30 day mortality risk prediction scoring model was constructed,and the predictive performance of the model was verified both in modeling and validation groups.Results 356 strains of Candida including 126 strains of C.albicans(35.39%),79 strains of C.tropicalis(22.19%),74 strains of C.parapsilosis(20.79%),48 strains of C.glabrata(13.48%),14 strains of C.guilliermondii(3.93%),8 strains of C.krusei(2.25%),and 7 strains of other Candida(1.97%)were detected in 336 patients with candidemia.The incidence of candidaemia among hospitalized patients increased from 0.20 ‰ in 2011 to 0.48 ‰ in 2020.The resistance rate of candida to amphotericin B was significantly lower than that of fluconazole,voriconazole and itraconazole(P＜0.05).Among the 324 cases included in the model,95 patients died in 30 days after diagnosis,and the mortality rate was 29.32%.The proportion of males,fever,and parenteral nutrition in modeling group was significantly higher than that in validation group(P＜0.05),while the proportion of chronic lung disease and surgical history within one month were lower than those in validation group(P＜0.05).Logistic regression analysis showed that chronic renal failure,mechanical ventilation,severe neutropenia,failure to receive anti-fungal treatment within 72 hours,and APACHE Ⅱ≥20 were risk factors for short-term death of candidaemia,the OR values were 3.179,1.970,2.979,2.080,and 2.399,and the risk scores were 6,4,6,4,and 5,respectively.The area under the curve(AUC)of the risk scoring model for modeling group was 0.792(95%CI 0.721-0.862),and the result of Hosmer-Lemeshow(H-L)test was P=0.305;The AUC of validation group was 0.796(95%CI 0.735-0.898),and the H-L test result was P=0.329.A risk score≤8 indicated a low risk group for short-term mortality,a score of 9-15 indicated a medium risk group,and a score≥16 indicated a high risk group.Conclusions The incidence of candidemia in hospitalized patients is increasing and the mortality is high.The risk prediction score model can effectively predict the short-term prognosis and facilitate the early identification of the prognosis.

Objective To explore the occurrence of adverse reactions to vaccination in children with special health status in Kunming and the corresponding countermeasures.Methods The information data of 952 children with special health conditions who were vaccinated at the Child Healthcare Department of the Kunming Children's Hospital from October 2021 to February 2023 were collected,and descriptive methods were used for epidemiological analysis.Results The detection rate of adverse reactions in children with special health conditions in Kunming was 10.92%(104/952),all of which were mild adverse reactions.The top three vaccines with the highest detection rates of adverse reactions were adsorbed acellular pertussis-diphtheria-tetanus vaccine(Pertussis-Diphtheria-Tetanus),measles-mumps-rubella attenuated live vaccine(Measles-Mumps-Rubella),and meningococcal polysaccharide vaccine group A and C(Meningococcal A+C),with detection rates of 7.00%,4.14%,and 3.08%,respectively.The detection rate of adverse reactions in children with premature birth,allergy,and anemia after vaccination was higher,with detection rates of 13.87%,11.03%,and 10.05%,respectively.The detection rate of adverse reactions after more than two vaccinations was 73.08%,which was higher than the first vaccination detection rate of 26.92%.The clinical manifestations of vaccine adverse reactions were mainly fever,redness,and induration,with detection rates of 39.42%,21.15%,and 18.27%,respectively.The detection rate of adverse reactions in children with special health conditions under the age of two was higher,accounting for 75.00%.The detection rate of adverse reactions within 24 hours after vaccination was higher,accounting for 62.50%.After timely symptomatic treatment and follow-up observation,all adverse reactions in children with special health conditions after vaccination recovered within 7 days.Conclusion Adverse reactions after vaccination in children with special health conditions in Kunming are predominantly mild.The detection rate of adverse reactions post-vaccination for pertussis,measles,mumps,and meningococcal A+C is notably high.Children under 2 years old with premature birth,allergy,and anemia are more likely to have adverse reactions after vaccination.These adverse reactions typically manifest within 24 hours post-vaccination and resolve within 7 days.

ObjectiveThe scale of microalgae farming industry is huge. During farming, it is easy for microalgae to be affected by miscellaneous bacteria and other contaminants. Because of that, periodic test is necessary to ensure the growth of microalgae. Present microscopy imaging and spectral analysis methods have higher requirements for experiment personnel, equipment and sites, for which it is unable to achieve real-time portable detection. For the purpose of real-time portable microalgae detection, a real-time microalgae detection system of low detection requirement and fast detection speed is needed. MethodsThis study has developed a microalgae detection system based on deep learning. A microscopy imaging device based on bright field was constructed. With imaged captured from the device, a neural network based on YOLOv3 was trained and deployed on microcomputer, thus realizing real-time portable microalgae detection. This study has also improved the feature extraction network by introducing cross-region residual connection and attention mechanism and replacing optimizer with Adam optimizer using multistage and multimethod strategy. ResultsWith cross-region residual connection, the mAP value reached 0.92. Compared with manual result, the detection error was 2.47%. ConclusionThe system could achieve real-time portable microalgae detection and provide relatively accurate detection result, so it can be applied to periodic test in microalgae farming.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment.Oral squamous cell carcinoma(OSCC),a representative hypoxic tumor,has a heterogeneous internal metabolic environment.To clarify the relationship between different metabolic regions and the tumor immune microenvironment(TME)in OSCC,Single cell(SC)and spatial transcriptomics(ST)sequencing of OSCC tissues were performed.The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data.The metabolic activity of each spot was calculated using scMetabolism,and k-means clustering was used to classify all spots into hyper-,normal-,or hypometabolic regions.CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others.Through CellPhoneDB and NicheNet cell-cell communication analysis,it was found that in the hypermetabolic region,fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts(iCAFs),and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12.The secretion of CXCL12 recruits regulatory T cells(Tregs),leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment.This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC,ST and TCGA bulk data,and highlights potential targets for therapy.