Comparison and use of explainable machine learning-based survival models for heart failure patients.

Objective: Explainable machine learning (XAI) was introduced in this study to improve the interpretability, explainability and transparency of the modelling results. The survex package in R was used to interpret and compare two survival models - the Cox proportional hazards regression (coxph) model and the random survival forest (rfsrc) model - and to estimate overall survival (OS) and its determinants in heart failure (HF) patients using these models. Methods: We selected 1159 HF patients hospitalised at the First Affiliated Hospital of Kunming Medical University. First, the performance of the two models was investigated using the C-index, the integrated C/D AUC, and the integrated Brier score. Second, a global explanation of the whole cohort was carried out using the time-dependent variable importance and the partial dependence survival profile. Finally, the SurvSHAP(t) and SurvLIME plots and the ceteris paribus survival profile were used to obtain a local explanation for each patient. Results: By comparing the C-index, the C/D AUC, and the Brier score, this study showed that the model performance of rfsrc was better than coxph. The global explanation of the whole cohort suggests that the C-reactive protein, lg BNP (brain natriuretic peptide), estimated glomerular filtration rate, albumin, age and blood chloride were significant unfavourable predictors of OS in HF patients in both the cxoph and the rfsrc models. By including individual patients in the model, we can provide a local explanation for each patient, which guides the clinician in individualising the patient's treatment. Conclusion: By comparison, we conclude that the model performance of rfsrc is better than that of coxph. These two predictive models, which address not only the whole population but also selected patients, can help clinicians personalise the treatment of each HF patient according to his or her specific situation.

Superconductivity in pressurized trilayer La4Ni3O10-δ single crystals.

The pursuit of discovering new high-temperature superconductors that diverge from the copper-based model1-3 has profound implications for explaining mechanisms behind superconductivity and may also enable new applications4-8. Here our investigation shows that the application of pressure effectively suppresses the spin-charge order in trilayer nickelate La4Ni3O10-δ single crystals, leading to the emergence of superconductivity with a maximum critical temperature (Tc) of around 30 K at 69.0 GPa. The d.c. susceptibility measurements confirm a substantial diamagnetic response below Tc, indicating the presence of bulk superconductivity with a volume fraction exceeding 80%. In the normal state, we observe a strange metal behaviour, characterized by a linear temperature-dependent resistance extending up to 300 K. Furthermore, the layer-dependent superconductivity observed hints at a unique interlayer coupling mechanism specific to nickelates, setting them apart from cuprates in this regard. Our findings provide crucial insights into the fundamental mechanisms underpinning superconductivity, while also introducing a new material platform to explore the intricate interplay between the spin-charge order, flat band structures, interlayer coupling, strange metal behaviour and high-temperature superconductivity.

Predictive value of the prognostic nutritional index combined with serum chloride levels for the prognosis of patients with acute decompensated heart failure.

BACKGROUND: The prognostic nutritional index (PNI) and serum chloride level are related to adverse outcomes in patients with heart failure. However, little is known about the relationship between the PNI and serum chloride level in predicting the poor prognosis of patients with acute decompensated heart failure (ADHF). METHODS AND RESULTS: We reviewed 1221 consecutive patients with ADHF admitted to the First Affiliated Hospital of Kunming Medical University from January 2017 to October 2021. After excluding patients with in hospital death, missing follow-up data, missing chloride data, missing lymphocyte (LYM) count data, or missing serum albumin data, 805 patients were included. PNI was calculated using the formula: serum albumin (ALB) (g/L) + 5 × LYM count (10^9/L). Patients were divided into 4 groups according to the quartiles of the PNI, and the highest PNI quartile (PNI Q4: PNI ≥ 47.3) was set as the reference group. The patients in the lowest PNI quartile (PNI Q1: PNI < 40.8) had the lowest cumulative survival rate, and mortality risk decreased progressively through the quartiles (log-rank χ2 142.283, P < 0.0001). Patients with ADHF were divided into 8 groups by quartiles of PNI and median levels of serum chloride. After adjustment, the hazard ratio (HR) for all-cause mortality in ADHF patients in Group 1 was 8.7 times higher than that in the reference Group 8. Furthermore, the addition of serum chloride level and PNI quartile to the Cox model increased the area under the Receiver operating characteristic (ROC) curve by 0.05, and the area under the ROC curve of the new model was higher than that of the original model with traditional risk factors. CONCLUSIONS: Both the lowest PNI quartiles and low chloride level indicate a higher risk of all-cause death in patients with ADHF.

miRNA-146a-5p Inhibits Hypoxia-Induced Myocardial Fibrosis Through EndMT.

Cardiac Vascular disease particularly myocardial infarction (MI) is a threat to health worldwide. microRNAs (miRNAs) have been shown to regulate myocardial fibrosis. Therefore, it is potential to investigate the mechanism of miRNA and fibrosis following myocardial infarction. Hypoxia human cardiac microvascular endothelial cells (HCMECs) were selected for the vitro experimental model. The miR-146a-5p expression was tested via RT-qPCR. The level of endothelial-to-mesenchymal transition (EndMT) and fibrosis markers were detected by Western blotting and immunofluorescence. Then, the inflammation, cell viability and apoptosis were investigated. The target was predicted by an online database and verified by a dual-luciferase activity assay. An MI mouse model was created to validate that miR-146a-5p regulates cardiac fibrosis in vivo. MI mouse was transfected with miR-146a-5p lentivirus. Subsequently, its effect on cardiac fibrosis of infarcted hearts was assessed by In situ hybridization (ISH), Immunohistochemistry (IHC), Triphenylterazolium chloride (TTC) staining and Masson staining. Herein, we confirmed that miR-146a-5p was down-regulated in hypoxia HCMECs. Overexpression of miR-146a-5p inhibited hypoxia-induced cardiac fibrosis following myocardial infarction by inhibiting EndMT in HCMECs. Thioredoxin-interacting protein (TXNIP) was a target that was negatively regulated by miR-146a-5p. Up-regulation of miR-146a-5p inhibited cardiac fibrosis via regulating EndMT by targeting TXNIP, and it also regulated EndMT to inhibit cardiac fibrosis in vivo.

Clinical prognostic impact of C-NLR in heart failure patients with different ejection fractions: a retrospective study.

OBJECTION: Inflammatory conditions and immune disorders may worsen the prognosis of chronic heart failure (CHF) patients. The aim of this study was to evaluate the prognostic value of a new indicator, C-NLR, composed of C-reactive protein (CRP) and neutrophil-to-lymphocyte ratio (NLR), for the risk of all-cause mortality in HF patients with different ejection fractions. METHODS: A total of 1221 CHF patients admitted to the First Affiliated Hospital of Kunming Medical University from January 2017 to October 2021 were enrolled in this study. All patients were divided into 2 groups according to the median C-NLR. Kaplan-Meier survival curves were used to compare the all-cause mortality among CHF patients with different ejection fractions. Cox proportional hazards analysis was used to evaluate the relationships between variables and mortality. The predictive value of the C-NLR was assessed by using receiver operating characteristic (ROC) analyses. RESULTS: We collected data from 1192 patients with CHF. Kaplan-Meier survival analysis revealed that patients with low LCR levels had better overall survival (OS). After multivariate adjustment Cox proportional hazards analysis, the level of C-NLR was still independently related to mortality. CONCLUSIONS: C-NLR was a competent independent predictor in HF with different ejection fractions, and routine measurement of C-NLR would help clinical doctors identify patients with a poor prognosis.

Prognostic value of the fibrinogen-to-albumin ratio (FAR) in patients with chronic heart failure across the different ejection fraction spectrum.

The ratio of fibrinogen to albumin (FAR) is considered a new inflammatory biomarker and a predictor of cardiovascular disease risk. However, its prognostic value for patients with chronic heart failure (CHF) with different ejection fractions (EFs) remains unclear. A total of 916 hospitalized patients with CHF from January 2017 to October 2021 in the First Affiliated Hospital of Kunming Medical University were included in the study. Death occurred in 417 (45.5%) patients out of 916 patients during a median follow-up time of 750 days. Among these patients, 381 patients suffered from HFrEF (LVEF <40%) and 535 patients suffered from HFpEF or HFmrEF (HFpEF plus HFmrEF, LVEF ≥ 40%). Patients were categorized into high-level FAR (FAR-H) and low-level FAR (FAR-L) groups based on the optimal cut-off value of FAR (9.06) obtained from receiver operating characteristic (ROC) curve analysis. Upon analysing the Kaplan - Meier plots, the incidence of death was significantly higher in all patients with FAR-H and patients in both HF subgroups (p < 0.001). The multivariate Cox proportional hazard analyses indicated that the FAR was an independent predictor of all-cause mortality, regardless of heart failure subtype. (HR 1.115, 95% CI 1.089-1.142, p < 0.001; HFpEF plus HFmrEF, HR 1.109, 95% CI 1.074-1.146, p < 0.0001; HFrEF, HR 1.138, 95% CI 1.094-1.183, p < 0.0001) The optimal cut-off value of FAR in predicting all-cause mortality was 9.06 with an area under the curve value of 0.720 (95% CI: 0.687-0.753, p < 0.001), a sensitivity of 68.8% and a specificity of 65.6%. After adjusting for the traditional indicators (LVEF, Lg BNP, etc.), the new model with the FAR had better prediction ability in patients with CHF. Elevated FAR is an independent predictor of death in CHF and is not related to the HF subtype.

Predictive Value of the Hb/RDW Ratio for the Risk of All-Cause Death in Patients with Heart Failure with Different Ejection Fractions.

INTRODUCTION: The prognostic value of the ratio of haemoglobin to red cell distribution width (HRR) in different types of heart failure (HF) is not well known. METHOD AND RESULTS: We analysed the long-term prognostic value of HRR in patients with HF using the Cox proportional risk model and Kaplan-Meier method. We reviewed consecutive 972 HF patients. The overall mortality rate was 45.68%. Mortality was 52.22% in the HFrEF group and 40.99% in the HFpEF + HFmrEF group. Cox regression showed that when HRR increased by 1 unit, the risk of all-cause death in all HF patients decreased by 22.8% (HR: 0.772, 95% CI: 0.724, 0.823, p < 0.001), in the HFpEF + HFmrEF group it decreased by 15.5% (HR: 0.845, 95% CI: 0.774, 0.923, p < 0.001), and in the HFrEF group it decreased by 36.1% (HR: 0.639, 95% CI: 0.576, 0.709, p < 0.0001). Subgroup analysis showed that there were interactions between the EF and HRR groups. The group in which HRR best predicted all-cause death from HF was group 1 (EF <40%, HRR <9.45), followed by group 2 (EF <40%, HRR ≥9.45), and group 3 (EF ≥40%, HRR <9.45). HRR had no predictive value in group 4 (EF ≥40%, HRR ≥9.45). CONCLUSION: HRR is an important predictor of all-cause mortality in patients with HF, especially HFrEF. There is an interaction between HRR group and LVEF group.

Interpretable prediction of 3-year all-cause mortality in patients with chronic heart failure based on machine learning.

BACKGROUND: The goal of this study was to assess the effectiveness of machine learning models and create an interpretable machine learning model that adequately explained 3-year all-cause mortality in patients with chronic heart failure. METHODS: The data in this paper were selected from patients with chronic heart failure who were hospitalized at the First Affiliated Hospital of Kunming Medical University, from 2017 to 2019 with cardiac function class III-IV. The dataset was explored using six different machine learning models, including logistic regression, naive Bayes, random forest classifier, extreme gradient boost, K-nearest neighbor, and decision tree. Finally, interpretable methods based on machine learning, such as SHAP value, permutation importance, and partial dependence plots, were used to estimate the 3-year all-cause mortality risk and produce individual interpretations of the model's conclusions. RESULT: In this paper, random forest was identified as the optimal aools lgorithm for this dataset. We also incorporated relevant machine learning interpretable tand techniques to improve disease prognosis, including permutation importance, PDP plots and SHAP values for analysis. From this study, we can see that the number of hospitalizations, age, glomerular filtration rate, BNP, NYHA cardiac function classification, lymphocyte absolute value, serum albumin, hemoglobin, total cholesterol, pulmonary artery systolic pressure and so on were important for providing an optimal risk assessment and were important predictive factors of chronic heart failure. CONCLUSION: The machine learning-based cardiovascular risk models could be used to accurately assess and stratify the 3-year risk of all-cause mortality among CHF patients. Machine learning in combination with permutation importance, PDP plots, and the SHAP value could offer a clear explanation of individual risk prediction and give doctors an intuitive knowledge of the functions of important model components.

Advanced lung cancer inflammation index combined with geriatric nutritional risk index predict all-cause mortality in heart failure patients.

BACKGROUND: This study was undertaken to explore the predictive value of the advanced lung cancer inflammation index (ALI) combined with the geriatric nutritional risk index (GNRI) for all-cause mortality in patients with heart failure (HF). METHODS AND RESULTS: We enrolled 1123 patients with HF admitted to our cardiology department from January 2017 to October 2021. Patients were divided into four groups, according to the median ALI and GNRI. From the analysis of the relationship between the ALI and GNRI, we concluded that there was a mild positive linear correlation (r = 0.348, p < 0.001) and no interaction (p = 0.140) between the ALI and GNRI. Kaplan‒Meier analysis showed that the cumulative incidence of all-cause mortality in patients with HF was highest in Group 1 (log-rank χ2 126.244, p < 0.001). Multivariate Cox proportional hazards analysis revealed that ALI and GNRI were independent predictors of all-cause mortality in HF patients (ALI: HR 0.407, 95% CI 0.296-0.560, p < 0.001; GNRI: HR 0.967, 95% CI 0.954-0.980, p < 0.001). The area under the curve (AUC) for ALI combined with GNRI was 0.711 (p < 0.001), according to the time-dependent ROC curve. CONCLUSION: ALI and GNRI were independent predictors of all-cause mortality in HF patients. Patients with HF had the highest risk of all-cause mortality when the ALI was < 24.60 and the GNRI was < 94.41. ALI combined with the GNRI has good predictive value for the prognosis of HF patients.

Synthesis of novel DOTA-/AAZTA-based bifunctional chelators: Solution thermodynamics, peptidomimetic conjugation, and radiopharmaceutical evaluation.

Bifunctional chelators (BFCs), which link metallic radionuclide and a targeting vector, are some of the most crucial components of metallic radionuclide-based radiopharmaceuticals for positron-emission computed tomography (PET) imaging. In this study, we designed and synthesized two versatile BFCs, p-NCS-Ph-DE4TA and p-NCS-Ph-AAZ4TA, and we conjugated them with a prostate-specific membrane antigen (PSMA) inhibitor. These two chelators showed high affinity for Ga (III) according to a study of the thermodynamics and kinetics and DFT calculations. The labeled PSMA targeted probes, [68Ga]Ga-p-NCS-Ph-DE4TA-PSMA and [68Ga]Ga-p-NCS-Ph-AAZ4TA-PSMA, maintained excellent stability in vitro, and they exhibited high specific activity when binding to PSMA. A PET/CT imaging study in mice bearing SMMC-7721 hepatocellular carcinoma xenografts demonstrated clear visualization of tumors with a high tumor uptake and low background level, indicating the excellent performance in vivo and specific activity when targeting hepatocellular carcinomas. In summary, p-NCS-Ph-DE4TA and p-NCS-Ph-AAZ4TA are leading developmental candidates for PET imaging for tumor diagnosis.

Prognostic Significance of Serum Chloride Level Reduction in Patients with Chronic Heart Failure with Different Ejection Fractions.

Little is known regarding the prognostic value of serum chloride in patients with chronic heart failure (CHF) with different ejection fractions. We sought to determine the postdischarge outcomes associated with lower serum chloride between different CHF types.We reviewed the medical records of 1221 consecutive patients with CHF admitted to the First Affiliated Hospital of Kunming Medical University from January 2017 to October 2021. After excluding patients with in-hospital death, missing follow-up data, missing serum chloride level data, or chronic dialysis therapy, 791 patients were included. Of these patients, 343 had heart failure with reduced ejection fraction (HFrEF; i.e., left ventricular ejection fraction (LVEF) < 40%), and 448 had heart failure with preserved ejection fraction (HFpEF) or heart failure with median ejection fraction (HFmrEF; HFpEF plus HFmrEF; i.e., LVEF ≥40%). Over a median follow-up of 750 days, 344 patients (43.5%) had all-cause mortality. In the univariate analysis, serum sodium and chloride were strongly associated with mortality in both HF subgroups (P < 0.0001). A multivariable model including both serum sodium and chloride showed the highly significant association between serum chloride and survival (P < 0.0001), whereas the association between serum sodium and mortality was not reported (HFpEF plus HFmrEF, hazard ratio (HR) 0.975, 95% confidence interval [CI] 0.942-1.010, P = 0.158; HFrEF, HR 1.007, 95% CI 0.966-1.051, P = 0.734). Kaplan-Meier survival curve analysis revealed a significant difference in mortality risk with decreasing chloride levels in all patients with CHF. The optimal cutoff value of chloride in predicting all-cause mortality was 102.95 mmol/L with area under the curve value of 0.76 [HR 0.760, 95% CI 0.727-0.793, P < 0.0001], sensitivity of 60.2%, and specificity of 78.3%.Lower serum chloride is an independent predictor of death in CHF, regardless of heart failure subtype.

Hairpin DNA-enabled ratiometric electrochemical aptasensor for detection of malathion.

A hairpin DNA-enabled ratiometric electrochemical aptasensor is reported for sensitive and reliable detection of malathion (MAL). The approach employs hairpin DNA (ferrocene-labeled, Fc-hDNA) as a carrier to hybridize MAL aptamers (methylene blue-labeled, MB-Apt) to form double-stranded DNA structures on an electrode. The presence of MAL induces the removal of aptamers, and hDNA re-forms hairpin structures, causing a decrease in the oxidation current of MB (IMB) and an increase in the oxidation current of Fc (IFc). The ratiometric signal of IFc/IMB responds quantitatively to MAL concentrations. To compare analytical performances, a linear single-stranded DNA (ssDNA) is also used to construct the ssDNA-based aptasensor. We demonstrate that hairpin DNA possessing a rigid two-dimensional structure can improve the assembly efficiency of aptamers and the stability of redox probes. The approach combines the advantages of the ratiometric electrochemical method with hairpin DNA-based conformational switching probes, enabling hDNA-based aptasensor with enhanced sensitivity and reliability, offering a linear range of 0.001 to 1.0 ng mL-1. The platform was applied to detect MAL in lettuce, and the statistical analysis indicated that no significant differences were found between the developed platform and HPLC-MS.

Inhibition of Poly(ADP-ribose) Polymerase Sensitizes [177Lu]Lu-DOTAGA.(SA.FAPi)2-Mediated Radiotherapy in Triple-Negative Breast Cancer.

Fibroblast activation protein (FAP) is highly expressed in many tumor types and constitutes a promising target for tumor-specific delivery of therapeutic radionuclides. [177Lu]Lu-DOTAGA.(SA.FAPi)2 is a novel radiopharmaceutical based on a novel bidentate inhibitor of FAP that is excreted more slowly than its monomeric counterparts. Still, the efficacy of radiotherapy is mitigated by cascades of DNA damage repair signaling in tumor cells including those via Poly(ADP-ribose) polymerase (PARP). We hereby aimed to evaluate the efficacy of [177Lu]Lu-DOTAGA.(SA.FAPi)2 in combination with a PARP inhibitor, Olaparib, in the 4T1 murine triple negative breast cancer (TNBC) model. The therapeutic efficacy was visualized using 18F-FDG and [68Ga]Ga-FAPI-04 positron emission imaging/computer tomography (PET/CT). Our results demonstrated that Olaparib suppressed BALB/3T3 fibroblasts in vitro and sensitized the efficacy of [177Lu]Lu-DOTAGA.(SA.FAPi)2 in mice bearing 4T1 tumors via enhancement of DNA damage. Treatment-associated toxicity was tolerable with only mild leukopenia. Therefore, the combination of [177Lu]Lu-DOTAGA.(SA.FAPi)2 and Olaparib is a feasible treatment against TNBC.

Dual-ratiometric electrochemical aptasensor based on carbon nanohorns/anthraquinone-2-carboxylic acid/Au nanoparticles for simultaneous detection of malathion and omethoate.

Organophosphorus pesticides (OPs) are one of the most frequently used pesticides in agriculture, and their residues in environment have caused serious human health and environmental concerns. In this work, we reported a dual-ratiometric electrochemical aptasensor based on carbon nanohorns/anthraquinone-2-carboxylic acid/Au nanoparticles (CNHs/AQ/AuNPs) for simultaneous detection of malathion (MAL) and omethoate (OMT). Here, CNHs/AQ/AuNPs composites were synthesized by a simple room temperature method, and used as a substrate to generate a reference signal (IAQ) and enlarge response signals. Hairpin DNA was then immobilized, offering independent and specific binding sites to further adsorb MB-labelled MAL aptamer (MB-Apt1) and Fc-labelled OMT aptamer (Fc-Apt2). Upon the addition of MAL or OMT, the formation of aptamer-target complex caused the release of MB-Apt1 or Fc-Apt2 from the electrode, resulting in a decrease in IMB or IFc, while IAQ kept unchanged. Based on this principle, the ratiometric signals of IMB/IAQ and IFc/IAQ were used to simultaneously detect MAL and OMT, offering a linear range of 3 pg mL-1 to 3 ng mL-1 for MAL and 10 pg mL-1 to 10 ng mL-1 for OMT, and no significant cross-reactivity existed. By taking advantage of the excellent conductivity and large specific area of CNHs/AQ/AuNPs and the stable two-dimensional structure of hairpin DNA, the aptasensor exhibited high sensitivity, selectivity and reliability. Our work has offered a novel way for simultaneous detection of multiple OPs.

[The Tertiary Hospital's Medical Materials Supply in the Prevention of Public Health Emergencies].

OBJECTIVE: To ensure the supply of prevention materials in the tertiary public hospitals in prefecturelevel cities, and to make the process of allocating prevention materials more scientific and reasonable. METHODS: Open the green passage, simplify the procurement process, carry out emergency procurement of related materials, ensure timely delivery of prevention materials, distribute them at different levels, and strengthen the warehouse management of prevention materials. RESULTS: The scheme of emergancy supplies was constantly improved, and the supply of prevention materials was completed with good quality. CONCLUSIONS: Using scientific and efficient management methods, the supply of prevention materials in medical institutions has been guaranteed, which has experience and reference significance for the prevention and control of similar public health emergencies in the future.

PET imaging of an optimized anti-PD-L1 probe 68Ga-NODAGA-BMS986192 in immunocompetent mice and non-human primates.

BACKGROUND: Adnectin is a protein family derived from the 10th type III domain of human fibronectin (10Fn3) with high-affinity targeting capabilities. Positron emission tomography (PET) probes derived from anti-programmed death ligand-1 (PD-L1) Adnectins, including 18F- and 68Ga-labeled BMS-986192, are recently developed for the prediction of patient response to immune checkpoint blockade. The 68Ga-labeled BMS-986192, in particular, is an attractive probe for under-developed regions due to the broader availability of 68Ga. However, the pharmacokinetics and biocompatibility of 68Ga-labeled BMS-986192 are still unknown, especially in non-human primates, impeding its further clinical translation. METHODS: We developed a variant of 68Ga-labeled BMS-986192 using 1,4,7-triazacyclononane,1-glutaric acid-4,7-acetic acid (NODAGA) as the radionuclide-chelator. The resultant probe, 68Ga-NODAGA-BMS986192, was evaluated in terms of targeting specificity using a bilateral mouse tumor model inoculated with wild-type B16F10 and B16F10 transduced with human PD-L1 (hPD-L1-B16F10). The dynamic biodistribution and radiation dosimetry of this probe were also investigated in non-human primate cynomolgus. RESULTS: 68Ga-NODAGA-BMS986192 was prepared with a radiochemical purity above 99%. PET imaging with 68Ga-NODAGA-BMS986192 efficiently delineated the hPD-L1-B16F10 tumor at 1 h post-injection. The PD-L1-targeting capability of this probe was further confirmed using in vivo blocking assay and ex vivo biodistribution studies. PET dynamic imaging in both mouse and cynomolgus models revealed a rapid clearance of the probe via the renal route, which corresponded to the low background signals of the PET images. The probe also exhibited a favorable radiation dosimetry profile with a total-body effective dose of 6.34E-03 mSv/MBq in male cynomolgus. CONCLUSIONS: 68Ga-NODAGA-BMS986192 was a feasible and safe tool for the visualization of human PD-L1. Our study also provided valuable information on the potential of targeted PET imaging using Adnectin-based probes.

Optimization Design of the Landscape Elements in the Lhasa Residential Area Driven by an Orthogonal Experiment and a Numerical Simulation.

Landscape elements have become an important means to improve the quality of life of residents because of their direct influence on the thermal environment, but the selection and configuration of landscape elements have different effects on human thermal comfort in different climate conditions. In this research, the typical residential area of Lhasa in Tibet was taken as the research object, the experimental scheme was prepared using an orthogonal test, and the simulation was carried out using ENVI-met to explore the influences of the green configuration, water area, and ground reflectance, as well as their interaction with the thermal environment in winter and summer under alpine climate conditions. Taking the physiological equivalent temperature (PET) as the optimization index, the optimal design scheme for the synergistic effect of the residential landscape elements was determined. The results were as follows. (1) The order of the landscape configuration factors was as follows: green configuration > water area > leaf area index > ground reflectance in summer. In winter, the order was green configuration > water area > ground reflectance > leaf area index (LAI). (2) With the combined driving of the orthogonal test and the numerical simulation, the optimal scheme of the landscape elements was determined, which was "tree shrub lawn, water area ratio 16%, ground reflectance 0.5, and LAI = 3 m2/m3". (3) Finally, the optimal design strategy of the landscape configuration was proposed for the typical outdoor active space of the Lhasa residential area.

Altering the Microbiome Inhibits Tumorigenesis in a Mouse Model of Oviductal High-Grade Serous Carcinoma.

Studies have shown bacteria influence the initiation and progression of cancers arising in sites that harbor rich microbial communities, such as the colon. Little is known about the potential for the microbiome to influence tumorigenesis at sites considered sterile, including the upper female genital tract. The recent identification of distinct bacterial signatures associated with ovarian carcinomas suggests microbiota in the gut, vagina, or elsewhere might contribute to ovarian cancer pathogenesis. Here, we tested whether altering the microbiome affects tumorigenesis in a mouse model of high-grade serous carcinoma (HGSC) based on conditional oviduct-specific inactivation of the Brca1, Trp53, Rb1, and Nf1 tumor suppressor genes. Cohorts of control (n = 20) and antibiotic-treated (n = 23) mice were treated with tamoxifen to induce tumor formation and then monitored for 12 months. The antibiotic cocktail was administered for the first 5 months of the monitoring period in the treatment group. Antibiotic-treated mice had significantly fewer and less advanced tumors than control mice at study endpoint. Antibiotics induced changes in the composition of the intestinal and vaginal microbiota, which were durable in the fecal samples. Clustering analysis showed particular groups of microbiota are associated with the development of HGSC in this model. These findings demonstrate the microbiome influences HGSC pathogenesis in an in vivo model that closely recapitulates the human disease. Because the microbiome can modulate efficacy of cancer chemo- and immunotherapy, our genetically engineered mouse model system may prove useful for testing whether altering the microbiota can improve the heretofore poor response of HGSC to immunotherapies. SIGNIFICANCE: This study provides strong in vivo evidence for a role of the microbiome in ovarian cancer pathogenesis.

Correlation Between Dual-Time-Point FDG PET and Tumor Microenvironment Immune Types in Non-Small Cell Lung Cancer.

PURPOSE: Dual-time-point 18F-fluorodeoxyglucose positron emission tomography (DTP 18F-FDG PET), which reflects the dynamics of tumor glucose metabolism, may also provide a novel approach to the characterization of both cancer cells and immune cells within the tumor immune microenvironment (TIME). We investigated the correlations between the metabolic parameters (MPs) of DTP 18F-FDG PET images and the tumor microenvironment immune types (TMITs) in patients with non-small cell lung cancer (NSCLC). METHODS: A retrospective analysis was performed in 91 patients with NSCLC who underwent preoperative DTP 18F-FDG PET/CT scans. MPs in the early scan (eSUVmax, eSUVmean, eMTV, eTLG) and delayed scan (dSUVmax, dSUVmean, dMTV, dTLG) were calculated, respectively. The change in MPs (ΔSUVmax, ΔSUVmean, ΔMTV, ΔTLG) between the two time points were calculated. Tumor specimens were analyzed by immunohistochemistry for PD-1/PD-L1 expression and CD8+ tumor-infiltrating lymphocytes (TILs). TIME was classified into four immune types (TMIT I ~ IV) according to the expression of PD-L1 and CD8+ TILs. Correlations between MPs with TMITs and the immune-related biomarkers were analyzed. A composite metabolic signature (Meta-Sig) and a combined model of Meta-Sig and clinical factors were constructed to predict patients with TMIT I tumors. RESULTS: eSUVmax, eSUVmean, dSUVmax, dSUVmean, ΔSUVmax, ΔSUVmean, and ΔTLG were significantly higher in PD-L1 positive patients (p = 0.0007, 0.0006, < 0.0001, < 0.0001, 0.0002, 0.0002, 0.0247, respectively), and in TMIT-I tumors (p = 0.0001, < 0.0001, < 0.0001, < 0.0001, 0.0009, 0.0009, 0.0144, respectively). Compared to stand-alone MP, the Meta-Sig and combined model displayed better performance for assessing TMIT-I tumors (Meta-sig: AUC = 0.818, sensitivity = 86.36%, specificity = 73.91%; Model: AUC = 0.869, sensitivity = 77.27%, specificity = 82.61%). CONCLUSION: High glucose metabolism on DTP 18F-FDG PET correlated with the TMIT-I tumors, and the Meta-Sig and combined model based on clinical and metabolic information could improve the performance of identifying the patients who may respond to immunotherapy.

A comparative analysis of pregnancy outcomes of patients with uterine fibroids after high intensity focused ultrasound ablation and laparoscopic myomectomy: a retrospective study.

PURPOSE: The aim of this study was to retrospectively compare and analyze pregnancy outcomes of patients with uterine fibroids after high intensity focused ultrasound (HIFU) ablation and laparoscopic myomectomy (LM). MATERIALS AND METHODS: The study group consisted of 346 patients with uterine fibroids who wished to conceive, in which 152 patients received HIFU ablation treatment (HIFU group) and 194 patients received LM treatment (LM group). The parents' baseline characters were recorded and the pregnancy outcomes were evaluated in a median follow-up time of 42 months (range: 16 ∼ 81) after the treatment, and the differences of the two groups were compared. RESULTS: Patients with uterine fibroids in HIFU group had a significant shorter pregnancy interval than that in LM group (10 months VS. 13 months, p < .05). No significant differences were observed in pregnancy rate, miscarriage rate, live birth rate, natural pregnancy rate, cesarean section rate, and perinatal complications rate between the HIFU group and the LM group (p > .05). When stratified by age, infertility history, fibroid types, fibroid numbers, and fibroid sizes, there was no statistically significant difference in pregnancy rate between the HIFU group and the LM group (p > .05). CONCLUSIONS: Based on the results from this study, both HIFU and LM can be safely used to treat patients who wish to conceive. The pregnancy outcomes of post-HIFU are similar to that of post-LM.