Multi-omics analysis of immune-related microbiome and prognostic model in head and neck squamous cell carcinoma.

OBJECTIVES: The aim of our study is to explore the transcriptional and microbial characteristics of head and neck cancer's immune phenotypes using a multi-omics approach. MATERIALS AND METHODS: Employing TCGA data, we analyzed head and neck squamous cell carcinoma (HNSCC) immune cells with CIBERSORT and identified differentially expressed genes using DESeq2. Microbial profiles, obtained from the TCMA database, were analyzed using LEfSe algorithm to identify differential microbes in immune cell infiltration (ICI) subgroups. Random Forest algorithm and deep neural network (DNN) were employed to select microbial features and developed a prognosis model. RESULTS: We categorized HNSCC into three immune subtypes, finding ICI-2 with the worst prognosis and distinct microbial diversity. Our immune-related microbiome (IRM) model outperformed the TNM staging model in predicting survival, linking higher IRM model scores with poorer prognosis, and demonstrating clinical utility over TNM staging. Patients categorized as low-risk by the IRM model showed higher sensitivity to cisplatin and sorafenib treatments. CONCLUSIONS: This study offers a comprehensive exploration of the ICI landscape in HNSCC. We provide a detailed scenario of immune regulation in HNSCC and report a correlation between differing ICI patterns, intratumor microbiome, and prognosis. This research aids in identifying prime candidates for optimizing treatment strategies in HNSCC. CLINICAL RELEVANCE: This study revealed the microbial signatures associated with immunophenotyping of HNSCC and further found the microbial signatures associated with prognosis. The prognostic model based on IRM microbes is helpful for early prediction of patient prognosis and assisting clinical decision-making.

Dual mechanisms based on synergistic effects of evaporation potential and streaming potential for natural water evaporation.

Electricity generation by natural water evaporation generators (NWEGs) using porous materials shows great potential for energy harvesting, but mechanistic investigations of NWEGs have mostly been limited to streaming potential studies. In this study, we propose the coexistence of an evaporation potential and streaming potential in a NWEG using ZSM-5 as the generation material. The iron probe method, salt concentration regulation, solution regulation, and side evaporation area regulation were used to analyze the NWEG mechanism. Our findings revealed that a streaming potential formed as water flowed inside the ZSM-5 nanochannels, driven by electrodynamic effects that increased from the bottom to the top of the generator. In addition, an evaporation potential existed at the surface interface between ZSM-5 and water, which decreased from the bottom to the top as the evaporation height of the generator increased. The resulting open-circuit voltage (Voc) depended on the balance between the evaporation and streaming potentials, both of which were influenced by the evaporation enthalpy (Ee) or vapor pressure. Generally, a higher Ee or lower vapor pressure led to a lower evaporation potential and subsequently a lower Voc. A dual mechanism involving synergistic evaporation potential and streaming potential is proposed to explain the mechanism of NWEGs.

Hospital spending and length of hospital stay for mental disorders in Hunan, China.

Objectives: To describe hospital spending and length of stay for mental disorders in Hunan, China. Methods: We extracted hospital care data for Hunan province from the Chinese National Health Statistics Network Reporting System. Patients with mental disorders (ICD-10 codes: F00 to F99) as the principal diagnosis and hospitalized between January 1, 2017 and December 31, 2019 were included. We retrieved information on age, sex, number of comorbidities, diagnosis, level of hospital, hospital costs, date of admission and discharge, length of stay (LOS), and method of payment of eligible participants. Spending at the provincial level, and spending and LOS at the individual level were described. Quantile regression and linear regression were conducted to investigate factors for hospital cost and LOS for major mental disorders. Results: The 2019 annual spending on mental disorders in Hunan province was 160 million US dollars, and 71.7% was paid by insurance. The annual spending on schizophrenia was 84 million dollars, contributing to a primary burden of mental disorders. The median spending for mental disorders was $1,085 per patient, and the median hospital stay was 22 days. The study identified several significant factors associated with hospital cost and LOS, including age, sex, comorbidity, and level of the hospital. In particular, a higher level of the hospital was associated with a higher hospital spending but a shorter LOS. Women with schizophrenia had a comparable hospital spending but a significantly shorter LOS than men with schizophrenia. Conclusion: Hospitalization spending for patients with mental disorders is substantial. Schizophrenia is the major burden of hospitalization for mental disorders. While patients treated at a higher level of hospital had higher spending, they stayed shorter in these hospitals.

Early Prediction Model for Critical Illness of Hospitalized COVID-19 Patients Based on Machine Learning Techniques.

Motivation: Patients with novel coronavirus disease 2019 (COVID-19) worsen into critical illness suddenly is a matter of great concern. Early identification and effective triaging of patients with a high risk of developing critical illness COVID-19 upon admission can aid in improving patient care, increasing the cure rate, and mitigating the burden on the medical care system. This study proposed and extended classical least absolute shrinkage and selection operator (LASSO) logistic regression to objectively identify clinical determination and risk factors for the early identification of patients at high risk of progression to critical illness at the time of hospital admission. Methods: In this retrospective multicenter study, data of 1,929 patients with COVID-19 were assessed. The association between laboratory characteristics measured at admission and critical illness was screened with logistic regression. LASSO logistic regression was utilized to construct predictive models for estimating the risk that a patient with COVID-19 will develop a critical illness. Results: The development cohort consisted of 1,363 patients with COVID-19 with 133 (9.7%) patients developing the critical illness. Univariate logistic regression analysis revealed 28 variables were prognosis factors for critical illness COVID-19 (p < 0.05). Elevated CK-MB, neutrophils, PCT, α-HBDH, D-dimer, LDH, glucose, PT, APTT, RDW (SD and CV), fibrinogen, and AST were predictors for the early identification of patients at high risk of progression to critical illness. Lymphopenia, a low rate of basophils, eosinophils, thrombopenia, red blood cell, hematocrit, hemoglobin concentration, blood platelet count, and decreased levels of K, Na, albumin, albumin to globulin ratio, and uric acid were clinical determinations associated with the development of critical illness at the time of hospital admission. The risk score accurately predicted critical illness in the development cohort [area under the curve (AUC) = 0.83, 95% CI: 0.78-0.86], also in the external validation cohort (n = 566, AUC = 0.84). Conclusion: A risk prediction model based on laboratory findings of patients with COVID-19 was developed for the early identification of patients at high risk of progression to critical illness. This cohort study identified 28 indicators associated with critical illness of patients with COVID-19. The risk model might contribute to the treatment of critical illness disease as early as possible and allow for optimized use of medical resources.

Spatial distribution of stroke readmission within 30 days and the influencing factors in Hunan Province. / 湖南省脑卒中30 då†å ¥é™¢çš„ç©ºé—´åˆ†å¸ƒåŠå ¶å½±å“å› ç´ .

OBJECTIVES: Stroke readmission increases financial burden on the family and the consumption of medical resources, and 30-day readmission rate is an important indicator for quality evaluation on health services. The influential factors for readmission mainly include patient-related factors, hospital factors, and society-related factors, with regional differences. This study aims to explore the spatial distribution and its main relevant factors for 30-day readmission of stroke patients in Hunan Province, and to provide the useful information for the improvement of regional prevention and control of stroke readmission. METHODS: Stroke patients in Hunan Province who were hospitalized in 2018 and readmitted within 30 days were included in the study. The vector map of the county boundary in Hunan Province was used as the basic map since county was the spatial analysis unit. SPSS 26.0 and ArcGIS 10.8 were used for statistical analysis that contains descriptive analysis of the general situation and the distribution map of readmission rate within 30 days of stroke patients. Spatial autocorrelation analysis and spatial regression analysis were further used to find the spatial clusters of the 30-day readmission rate of stroke and the local relationship between the readmission rate and main influential factors. RESULTS: In 2018, a total of 172 800 stroke patients were hospitalized in Hunan Province, of which 6 953 patients were re-hospitalized within 30 days after discharging due to stroke. The 30-day readmission rate was 4.09% in Hunan Province. The clusters of stroke readmission rates were mainly concentrated in the northeast and western regions in Hunan Province. The geographically weighted regression revealed that proportion of patients with complications, number of hospitals per 10 000 population and number of primary medical and health care institution per 10 000 population were the main relevant factors for stroke readmission, and there were differences both in the direction and degree of the effect on readmission in different regions. CONCLUSIONS: The 30-day readmission rate for stroke patients in Hunan province and its main influential factors had spatial heterogeneity. The key prevention and control areas were mainly concentrated in the northeast and western regions. It is recommended that the prevention and treatment of stroke complications and the construction of medical institutions need to be strengthened to improve the quality of medical services, particularly in the western region. The importance to the treatment of stroke complications should be attached in the northern region, and the primary health care should be reinforced in the northeast region. All counties should take prevention and control measures according to local conditions, so as to effectively control the readmission rate of stroke within 30 days.

Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China.

Purpose: To identify more objectively predictive factors of severe outcome among patients hospitalized for coronavirus disease 2019 (COVID-19). Patients and Methods: A retrospective cohort of 479 hospitalized patients diagnosed with COVID-19 in Hunan Province was selected. The prognostic effects of factors such as age and laboratory indicators were analyzed using the Kaplan-Meier method and Cox proportional hazards model. A prognostic nomogram model was established to predict the progression of patients with COVID-19. Results: A total of 524 patients in Hunan province with COVID-19 from December 2019 to October 2020 were retrospectively recruited. Among them, 479 eligible patients were randomly assigned into the training cohort (n = 383) and validation cohort (n = 96), at a ratio of 8:2. Sixty-eight (17.8%) and 15 (15.6%) patients developed severe COVID-19 after admission in the training cohort and validation cohort, respectively. The differences in baseline characteristics were not statistically significant between the two cohorts with regard to age, sex, and comorbidities (P > 0.05). Multivariable analyses included age, C-reactive protein, fibrinogen, lactic dehydrogenase, neutrophil-to-lymphocyte ratio, urea, albumin-to-globulin ratio, and eosinophil count as predictive factors for patients with progression to severe COVID-19. A nomogram was constructed with sufficient discriminatory power (C index = 0.81), and proper consistency between the prediction and observation, with an area under the ROC curve of 0.81 and 0.86 in the training and validation cohort, respectively. Conclusion: We proposed a simple nomogram for early detection of patients with non-severe COVID-19 but at high risk of progression to severe COVID-19, which could help optimize clinical care and personalized decision-making therapies.

Analysis of hospitalization costs and length of stay for oral cancer patients undergoing surgery: Evidence from Hunan, China.

OBJECTIVE: To provide information about hospitalization costs and length of stay (LOS) for inpatients undergoing oral cancer (OC) surgery, and to investigate the association of hospitalization costs and LOS with demographics, tumor subsite, surgery-related and hospital characteristics. METHODS: Data extracted from Chinese National Health Statistics Network Reporting System database in Hunan, China during 2017-2019 were analyzed using quantile regression models. RESULTS: A total of 6,420 OC patients undergoing surgery were identified. After controlling potential compounding variables, the median hospitalization cost was significantly higher in male than in female patientsby $515.70 at the median (p < 0.001). Patients aged over 60 hadsignificantly less costs by $294.85 at the meanthan did those below 60, while no differenceof LOS existed across age. OC patients with neck dissection had significantly higher costs by $1,983.33 at the median than those without (P < 0.001). Regional flaps were the most economical, with lower costs than free flaps by $3,084 (P < 0.001) and the pectoralis major myocutaneous flap (PMMF) by $549.45 (P < 0.001) at the median. CONCLUSION: Male is a significant driver of hospitalization costs and LOS for OC, and age over 60 is associated with lower costs, but not with LOS. Mouth primary site is associated with the highest costs and LOS, while lip primary site the lowest. Absence of neck dissection in early-stage OC can significantly reduce costs and LOS, but its oncological validity needs more evidence. Regional flaps are less expensive than free flaps and the PMMF for oral reconstruction, and are recommended in select patients.

Understanding variations and influencing factors on length of stay for T2DM patients based on a multilevel model.

AIM: Shortening the length of stay (LOS) is a potential and sustainable way to relieve the pressure that type 2 diabetes mellitus (T2DM) patients placed on the public health system. METHOD: Multi-stage random sampling was used to obtain qualified hospitals and electronic medical records for patients discharged with T2DM in 2018. A box-cox transformation was adopted to normalize LOS. Multilevel model was used to verify hospital cluster effect on LOS variations and screen potential factors for LOS variations from both individual and hospital levels. RESULT: 50 hospitals and a total of 12,888 T2DM patients were included. Significant differences in LOS variations between hospitals, and a hospital cluster effect on LOS variations (t = 92.188, P<0.001) was detected. The results showed that female patients, patients with new rural cooperative' medical insurance, hospitals with more beds, and hospitals with faster bed turnovers had shorter LOS. Conversely, elderly patients, patients with urban workers' medical insurance, patients requiring surgery, patients with the International Classification of Diseases coded complication types E11.1, E11.2, E11.4, E11.5, and other complications cardiovascular diseases, grade III hospitals, hospitals with a lower doctor-to-nurse ratio, and hospitals with more daily visits per doctor had longer LOS. CONCLUSIONS: The evidence proved that hospital cluster effect on LOS variation did exist. Complications and patients features at individual level, as well as organization and resource characteristics at hospital level, had impacted LOS variations to varying degrees. To shorten LOS and better meet the medical demand for T2DM patients, limited health resources must be allocated and utilized rationally at hospital level, and the patients with the characteristics of longer LOS risk must be identified in time. More influencing factors on LOS variations at different levels are still worth of comprehensive exploration in the future.

Risk of Metformin in Patients With Type 2 Diabetes With COVID-19: A Preliminary Retrospective Report.

The current outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has spread across the world. No specific antiviral agents have been adequately evidenced for the treatment of coronavirus disease 2019 (COVID-19). Although metformin has been recommended as a host-directed therapy for COVID-19, there are some opposite views. The effects of metformin on the disease severity of patients with COVID-19 with diabetes during hospitalization remains unclear. This study aimed to determine the effect of metformin on disease severity. We enrolled 110 hospitalized patients with COVID-19 with diabetes prescribed either metformin or non-metformin hypoglycemic treatment for a case-control study. The primary outcome was the occurrence of life-threatening complications. There were no differences between the two groups in age, sex, comorbidities, and clinical severity at admission. Blood glucose and lactate dehydrogenase levels of the metformin group were higher than those of the non-metformin group at admission. Other laboratory parameters at admission and treatments after admission were not different between the two groups. Strikingly, the percentage of patients who experienced life-threatening complications was significantly higher in the metformin group (28.6% (16/56) vs. 7.4% (4/54), P = 0.004). Antidiabetic therapy with metformin was associated with a higher risk of disease progression in patients with COVID-19 with diabetes during hospitalization (adjusted odds ratio = 3.964, 95% confidence interval 1.034-15.194, P = 0.045). This retrospective analysis suggested a potential safety signal for metformin, the use of which was associated with a higher risk of severe COVID-19. We propose that metformin withdrawal in patients with COVID-19 be considered to prevent disease progression.

PML overexpression inhibits proliferation and promotes the osteogenic differentiation of human mesenchymal stem cells.

The promyelocytic leukemia (PML) gene, as an important tumor-suppressor, has been proven to regulate stem cell function in multiple tissues; however its role in human mesenchymal stem cells (hMSCs) remains unclear. In the present study, the effect of PML on regulating the proliferation and osteogenic differentiation of hMSCs was explored. New downstream genes that may be responsible for the regulation of PML were found, and possible mechanisms were analyzed. The lentiviral vector which encodes full-length human PML cDNA or shRNA against PML was transfected into hMSCs. RT-PCR and western blotting were used to detect mRNA and protein expression. Flow cytometry was used to analyze apoptosis and the cell cycle distribution. Osteogenic differentiation of hMSCs was induced by osteo-inductive medium for 7 to 14 days. cDNA microarray was used to scan the gene expression profile and to identify significant changes in gene expression. In the present study, we found that PML was stably expressed in hMSCs, and the expression was increased time-dependently along with cell osteogenic differentiation. Overexpression of PML inhibited hMSC proliferation by inducing apoptosis and arresting the cell cycle. However, PML enhanced the osteoblast differentiation potential of hMSCs. PML-overexpressing hMSCs had a significant increase in mineralized matrix production and ALP activity on day 7 under osteogenic or non-osteogenic differentiation conditions. Upregulation of integrin-binding sialoprotein (IBSP, bone sialoprotein) induced by PML overexpression was found. Our data indicate that PML regulates hMSCs as an inhibitor of cell proliferation but a promoter of osteogenic differentiation.

[Study on matrine alleviating retinoic acid resistance in acute promyelocytic leukemia].

OBJECTIVE: To explore the role of matrine (MAT) alleviating all-trans retinoic acid (ATRA) resistance in acute promyelocytic leukemia (APL) and its mechanism. METHODS: ATRA sensitive strain of APL (NB4) and resistant strain (NB4-R1, NB4-R2) were used in this study. The low toxic dosage of MAT was established by MTT test, and ATRA IC(50) of the cell strains (cultured with or without 0.1 mmol/L MAT) were obtained to confirm the reversal index (RI); the influence of MAT (10, 8, 6, 4, 2, 1, 0.1, 0.01, 0.001 mmol/L) combine with 1 µmol/L ATRA on the differentiation of the three cell strains were observed by nitro blue tetrazolium chloride (NBT) test and morphologic changes. The apoptosis rate of cells treated with different concentration of MAT combined with 1 µmol/L ATRA was tested by flow cytometry with Annexin V/PI staining. RESULTS: (1) The toxicity of MAT to NB4, NB4-R1, and NB4-R2 cells was increased with the concentration, IC(50) value was (0.661 ± 0.035) mmol/L, (0.673 ± 0.132) mmol/L and (0.329 ± 0.020) mmol/L, respectively; (2) After treated with 0.1 mmol/L MAT, the ATRA resistance factor of NB4-R1 decreased markedly (RI = 4.96 ± 1.15), but did not of NB4-R2(RI = 0.66 ± 0.17); (3) The differentiation capacity of NB4 and NB4-R1 was enhanced with increase of MAT, and peaked at 0.1 mmol/L (P < 0.05), but did not of NB4-R2; (4) After treated with MAT, the ATRA (1 µmol/L) induced apoptosis rate in NB4 and NB4-R1 increased significantly (P < 0.05 and P < 0.01, respectively). CONCLUSION: MAT can reverse the ATRA resistance of NB4-R1, which may relate to the effect of MAT on differentiation and apoptosis. Treatment with MAT plus ATRA may exaggerate the cells resistance potency.