Design and rationale of the cardiometabolic health program linked with community health workers and mobile health telemonitoring to reduce health disparities (LINKED-HEARTS) program.

BACKGROUND: Hypertension and diabetes are major risk factors for cardiovascular diseases, stroke, and chronic kidney disease (CKD). Disparities in hypertension control persist among Black and Hispanic adults and persons living in poverty in the United States. The "LINKED-HEARTS Program" (a Cardiometabolic Health Program LINKED with Community Health WorkErs and Mobile HeAlth TelemonitoRing To reduce Health DisparitieS"), is a multi-level intervention that includes home blood pressure (BP) monitoring (HBPM), blood glucose telemonitoring, and team-based care. This study aims to examine the effect of the LINKED-HEARTS Program intervention in improving BP control compared to enhanced usual care (EUC) and to evaluate the reach, adoption, sustainability, and cost-effectiveness of the program. METHODS: Using a hybrid type I effectiveness-implementation design, 428 adults with uncontrolled hypertension (systolic BP ≥ 140 mm Hg) and diabetes or CKD will be recruited from 18 primary care practices, including community health centers, in Maryland. Using a cluster-randomized trial design, practices are randomly assigned to the LINKED-HEARTS intervention arm or EUC arm. Participants in the LINKED-HEARTS intervention arm receive training on HBPM, BP and glucose telemonitoring, and community health worker and pharmacist telehealth visits on lifestyle modification and medication management over 12 months. The primary outcome is the proportion of participants with controlled BP (<140/90 mm Hg) at 12 months. CONCLUSIONS: The study tests a multi-level intervention to control multiple chronic diseases. Findings from the study may be leveraged to reduce disparities in the management and control of chronic diseases and make primary care more responsive to the needs of underserved populations. TRIAL REGISTRATION: ClinicalTrials.gov. Identifier: NCT05321368.

Ferritinophagy and Ferroptosis in Cerebral Ischemia Reperfusion Injury.

Cerebral ischemia-reperfusion injury (CIRI) is the second leading cause of death worldwide, posing a huge risk to human life and health. Therefore, investigating the pathogenesis underlying CIRI and developing effective treatments are essential. Ferroptosis is an iron-dependent mode of cell death, which is caused by disorders in iron metabolism and lipid peroxidation. Previous studies demonstrated that ferroptosis is also a form of autophagic cell death, and nuclear receptor coactivator 4(NCOA4) mediated ferritinophagy was found to regulate ferroptosis by interfering with iron metabolism. Ferritinophagy and ferroptosis are important pathogenic mechanisms in CIRI. This review mainly summarizes the link and regulation between ferritinophagy and ferroptosis and further discusses their mechanisms in CIRI. In addition, the potential treatment methods targeting ferritinophagy and ferroptosis for CIRI are presented, providing new ideas for the prevention and treatment of clinical CIRI in the future.

Geriatric Nutritional Risk Index Combined with Calf Circumference Can be a Good Predictor of Prognosis in Patients Undergoing Surgery for Gastric or Colorectal Cancer.

OBJECTIVES: To explore the effect of combined hematological and physical measurement indicators on the prognosis of patients undergoing surgery for gastric or colorectal cancer and to screen for the best prognostic indicators. INTRODUCTION: Gastric and colorectal cancer is a widespread health concern worldwide and one of the major contributors to cancer-related death. The hematological and physical measurement indicators have been shown to associate with the prognosis of patients undergoing surgery for gastric or colorectal cancer, respectively, but it is still unclear whether the combination of the two can reflect the prognosis more effectively. METHODS: Thirteen hematological indicators and 5 physical measurement indicators were selected in this study, and the most promising ones were screened using LASSO regression. Then, the best prognostic indicators were selected by time-ROC curves. Survival curves were constructed using the Kaplan-Meier method, and the effects of hematological and physical measurement indicators on the prognosis of patients undergoing surgery for gastric or colorectal cancers were evaluated by Cox proportional risk regression analysis. In addition, the relationship between hematological and physical measurement indicators on secondary outcomes, including length of stay, hospitalization costs, intensive care unit (ICU) admission, and patients' subjective global assessment scores (PGSGA), was explored. RESULTS: After initial screening, among the hematological indicators, the geriatric nutritional risk index (GNRI) showed the highest mean area under the curve (AUC) values. Among body measures, calf circumference (CC) showed the highest mean AUC value. Further analyses showed that the combination of combined nutritional prognostic index (GNRI) and calf circumference (CC) (GNRI-CC) had the best performance in predicting the prognosis of patients undergoing surgery for gastric or colorectal cancers. Low GNRI, low CC, and low GNRI-low CC increased the risk of death by 44%, 48%, and 104%, respectively. Sensitivity analyses showed the same trend. In addition, low GNRI-low CC increased the risk of malnutrition by 17%. CONCLUSION: This study emphasizes that a combination of blood measures and body measures is essential to accurately assess the prognosis of patients undergoing surgery for gastric or colorectal cancers. The GNRI-CC is a good prognostic indicator and can also assess the risk of possible malnutrition.

Macroalgal microbiome biogeography is shaped by environmental drivers rather than geographical distance.

BACKGROUND AND AIMS: Contrasting patterns of host and microbiome biogeography can provide insight into the drivers of microbial community assembly. Distance-decay relationships are a classic biogeographical pattern shaped by interactions between selective and non-selective processes. Joint biogeography of microbiomes and their hosts is of increasing interest owing to the potential for microbiome-facilitated adaptation. METHODS: In this study, we examine the coupled biogeography of the model macroalga Durvillaea and its microbiome using a combination of genotyping by sequencing (host) and 16S rRNA amplicon sequencing (microbiome). Alongside these approaches, we use environmental data to characterize the relationship between the microbiome, the host, and the environment. KEY RESULTS: We show that although the host and microbiome exhibit shared biogeographical structure, these arise from different processes, with host biogeography showing classic signs of geographical distance decay, but with the microbiome showing environmental distance decay. Examination of microbial subcommunities, defined by abundance, revealed that the abundance of microbes is linked to environmental selection. As microbes become less common, the dominant ecological processes shift away from selective processes and towards neutral processes. Contrary to expectations, we found that ecological drift does not promote structuring of the microbiome. CONCLUSIONS: Our results suggest that although host macroalgae exhibit a relatively 'typical' biogeographical pattern of declining similarity with increasing geographical distance, the microbiome is more variable and is shaped primarily by environmental conditions. Our findings suggest that the Baas Becking hypothesis of 'everything is everywhere, the environment selects' might be a useful hypothesis to understand the biogeography of macroalgal microbiomes. As environmental conditions change in response to anthropogenic influences, the processes structuring the microbiome of macroalgae might shift, whereas those governing the host biogeography are less likely to change. As a result, increasingly decoupled host-microbe biogeography might be observed in response to such human influences.

Association of systemic inflammation with the obesity paradox in cancer: results from multi-cohort studies.

AIMS: This study aimed to explore whether the obesity paradox exists in overall and specific cancers and to investigate the role of systemic inflammation in the obesity paradox. METHODS: The Cox proportional hazard model was used to explore the relationship between body mass index (BMI) and all-cause mortality. The mediated effect was used to investigate the proportion of systemic inflammation mediating the relationship between BMI and cancer survival risk. RESULTS: The survival probability showed a step-like increase with an increase in BMI regardless of pathological stage. Approximately 10.8%-24.0% of the overall association between BMI and all-cause mortality in cancer was mediated by inflammation. In the internal validation, we found evidence of the obesity paradox in all body composition obtained using BIA, with inflammation remaining an important mediating factor. Furthermore, we also validated the existence of the obesity paradox of cancer in NHANES. Systemic inflammation remains an important factor in mediating the association between BMI and prognosis in cancer patients. CONCLUSIONS: The obesity paradox is prevalent in most cancers, except for hepatic biliary cancer and breast cancer. Inflammation may be one of the true features of the obesity paradox in cancer.

Remnant cholesterol is an effective biomarker for predicting survival in patients with breast cancer.

BACKGROUND: Breast cancer is the most common malignancy in women worldwide. The relationship between remnant cholesterol (RC) and the prognosis of patients with breast cancer has not been clearly reported. This study investigated the prognostic value of RC in predicting mortality in patients with breast cancer. METHODS: This study prospectively analysed 709 women patients with breast cancer from the Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) project. Restricted cubic splines were used to analyse the dose-response relationship between RC and breast cancer mortality. The Kaplan-Meier method was used to evaluate the overall survival of patients with breast cancer. A Cox regression analyses was performed to assess the independent association between RC and breast cancer mortality. Inverse probability of treatment weighting (IPTW) using the propensity score was used to reduce confounding. Sensitivity analysis was performed after excluding patients with underlying diseases and survival times shorter than one year. RESULTS: A linear dose-response relationship was identified between RC and the risk of all-cause mortality in patients with breast cancer (p = 0.036). Kaplan-Meier survival analysis and log-rank test showed that patients with high RC levels had poorer survival than those with low RC levels (p = 0.007). Univariate and multivariate Cox regression analyses showed that RC was an independent risk factor for mortality in women patients with breast cancer. IPTW-adjusted analyses and sensitivity analyses showed that CR remained a prognostic factor. CONCLUSIONS: RC is an independent risk factor for the prognosis of patients with breast cancer, and patients with higher RC levels have poorer survival.

Social determinants of health and emergency department visits among older adults with multimorbidity: insight from 2010 to 2018 National Health Interview Survey.

BACKGROUND: Multimorbidity is prevalent among older adults and is associated with adverse health outcomes, including high emergency department (ED) utilization. Social determinants of health (SDoH) are associated with many health outcomes, but the association between SDoH and ED visits among older adults with multimorbidity has received limited attention. This study aimed to examine the association between SDoH and ED visits among older adults with multimorbidity. METHODS: A cross-sectional analysis was conducted among 28,917 adults aged 50 years and older from the 2010 to 2018 National Health Interview Survey. Multimorbidity was defined as the presence of two or more self-reported diseases among 10 common chronic conditions, including diabetes, hypertension, asthma, stroke, cancer, arthritis, chronic obstructive pulmonary disease, and heart, kidney, and liver diseases. The SDoH assessed included race/ethnicity, education level, poverty income ratio, marital status, employment status, insurance status, region of residence, and having a usual place for medical care. Logistic regression models were used to examine the association between SDoH and one or more ED visits. RESULTS: Participants' mean (± SD) age was 68.04 (± 10.66) years, and 56.82% were female. After adjusting for age, sex, and the number of chronic conditions in the logistic regression model, high school or less education (adjusted odds ratio [AOR]: 1.10, 95% confidence interval [CI]: 1.02-1.19), poverty income ratio below the federal poverty level (AOR: 1.44, 95% CI: 1.31-1.59), unmarried (AOR: 1.19, 95% CI: 1.11-1.28), unemployed status (AOR: 1.33, 95% CI: 1.23-1.44), and having a usual place for medical care (AOR: 1.46, 95% CI 1.18-1.80) was significantly associated with having one or more ED visits. Non-Hispanic Black individuals had higher odds (AOR: 1.28, 95% CI: 1.19-1.38), while non-Hispanic Asian individuals had lower odds (AOR: 0.71, 95% CI: 0.59-0.86) of one or more ED visits than non-Hispanic White individuals. CONCLUSION: SDoH factors are associated with ED visits among older adults with multimorbidity. Systematic multidisciplinary team approaches are needed to address social disparities affecting not only multimorbidity prevalence but also health-seeking behaviors and emergent healthcare access.

Cholesterol-modified prognostic nutritional index (CPNI) as an effective tool for assessing the nutrition status and predicting survival in patients with breast cancer.

BACKGROUND: Malnutrition is associated with poor overall survival (OS) in breast cancer patients; however, the most predictive nutritional indicators for the prognosis of patients with breast cancer are not well-established. This study aimed to compare the predictive effects of common nutritional indicators on OS and to refine existing nutritional indicators, thereby identifying a more effective nutritional evaluation indicator for predicting the prognosis in breast cancer patients. METHODS: This prospective study analyzed data from 776 breast cancer patients enrolled in the "Investigation on Nutritional Status and its Clinical Outcome of Common Cancers" (INSCOC) project, which was conducted in 40 hospitals in China. We used the time-dependent receiver operating characteristic curve (ROC), Kaplan-Meier survival curve, and Cox regression analysis to evaluate the predictive effects of several nutritional assessments. These assessments included the patient-generated subjective nutrition assessment (PGSGA), the global leadership initiative on malnutrition (GLIM), the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI). Utilizing machine learning, these nutritional indicators were screened through single-factor analysis, and relatively important variables were selected to modify the PNI. The modified PNI, termed the cholesterol-modified prognostic nutritional index (CPNI), was evaluated for its predictive effect on the prognosis of patients. RESULTS: Among the nutritional assessments (including PGSGA, GLIM, CONUT, NRI, and PNI), PNI showed the highest predictive ability for patient prognosis (time-dependent ROC = 0.58). CPNI, which evolved from PNI, emerged as the superior nutritional index for OS in breast cancer patients, with the time-dependent ROC of 0.65. It also acted as an independent risk factor for mortality (p < 0.05). Moreover, the risk of malnutrition and mortality was observed to increase gradually among both premenopausal and postmenopausal age women, as well as among women categorized as non-overweight, overweight, and obese. CONCLUSIONS: The CPNI proves to be an effective nutritional assessment tool for predicting the prognosis of patients with breast cancer.

"Urban Respiration" Revealed by Atmospheric O2 Measurements in an Industrial Metropolis.

Urban regions, which "inhale" O2 from the air and "exhale" CO2 and atmospheric pollutants, including harmful gases and fine particles, are the largest sinks of atmospheric O2, yet long-term O2 measurements in urban regions are currently lacking. In this study, we report continuous measurements of atmospheric O2 in downtown Lanzhou, an industrial metropolis in northwestern China. We found declines in atmospheric O2 associated with deteriorated air quality and robust anticorrelations between O2 and gaseous oxides. By combining O2 and pollutants measurements with a Lagrangian atmospheric transport model, we quantitatively break down "urban respiration" (ΔO2URB) into human respiration (ΔO2RES) and fossil fuel combustion (ΔO2FF). We found increased ΔO2FF contribution (from 66.92% to 72.50%) and decreased ΔO2RES contribution (from 33.08 to 27.50%) as O2 declines and pollutants accumulate. Further attribution of ΔO2FF reveals intracity transport of atmospheric pollutants from industrial sectors and suggests transportation sectors as the major O2 sink in downtown Lanzhou. The varying relationships between O2 and pollutants under different conditions unfold the dynamics of urban respiration and provide insights into the O2 and energy consumption, pollutant emission, and intracity atmospheric transport processes.

The value of CRP-albumin-lymphocyte index (CALLY index) as a prognostic biomarker in patients with non-small cell lung cancer.

OBJECTIVE: The C-reactive protein-albumin-lymphocyte (CALLY) index is a new index related to inflammation, immunity, and nutrition. We investigated whether it can predict the prognosis of patients with non-small cell lung cancer (NSCLC) and developed a prognostic model including CALLY index. RESEARCH METHODS AND PROCEDURES: Data from patients with NSCLC who were followed up in the INSCOC database from May 2013 to December 2018 were retrospectively analyzed. Simple random sampling by splitting these patients into training (n = 1307) and validation cohorts (n = 557) resulted in a sample size ratio of 7:3. Using the results of COX regression analysis of the training cohort, a nomogram model for predicting 3- and 5-year overall survival (OS) was established and validated internally. The calibration and clinical decision curve were used to evaluate the prediction accuracy and clinical application ability of the nomogram and compared with the TNM staging system for lung cancer. RESULTS: Sex, TNM stage, surgical treatment, BMI, CALLY, and HGS were independent risk factors for the prognosis of NSCLC patients. The OS of NSCLC patients with a low CALLY index score was significantly worse than that of patients with a high CALLY index (P < 0.001). The CALLY-based nomogram had a good predictive prognostic power, with a C-index of 0.697. Compared with the traditional TNM staging system, our prognostic nomogram had better resolution and accuracy in predicting the 3-year and 5-year OS. Decision curve analysis showed that this prognostic model has a clinical application value. CONCLUSIONS: The CALLY index is a valuable biomarker for evaluating the prognosis of patients with lung cancer. The nomogram based on the CALLY index is highly effective in predicting OS in patients with NSCLC. The results of this study provide a reference tool for clinicians to guide the personalized treatment of patients with lung cancer.

Association between the Dietary Inflammatory Index and All-Cause Mortality in Adults with Obesity.

INTRODUCTION: The dietary inflammatory index (DII) is associated with numerous chronic noncommunicable diseases. Previous studies have shown that the pro-inflammatory DII categories are associated with abdominal and simple obesity. However, the association between DII and mortality in patients with abdominal obesity and simple overweight or obesity remains unclear. METHODS: We used data from the US National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. A DII >0 (positive DII) was defined as a pro-inflammatory diet. A restricted cubic spline curve was used to describe the trend between DII and all-cause mortality. We then examined the association between DII and all-cause mortality in different body types using a Cox regression analysis and investigated the differences between sexes. Finally, the mediating effects of systemic inflammation were explored. RESULTS: A pro-inflammatory diet increased all-cause mortality in adults with abdominal obesity (aHR: 1.31, 95% confidence interval [CI]: 1.11-1.54; p < 0.001) and with simple overweight or obesity (aHR: 1.30, 95% CI: 1.11-1.53; p < 0.001). In addition, the most pro-inflammatory DII increased the risk of mortality by 43% (hazard ratio [HR]: Q4 vs. Q1 = 1.43, 95% CI = 1.14-1.79; p = 0.002; p for trend = 0.003) and 39% (HR: Q4 vs. Q1 = 1.39, 95% CI = 1.13-1.74; p = 0.003; p for trend = 0.009) in participants with abdominal obesity and with simple overweight or obesity, respectively. However, this association was not present in normal-sized participants. Compared with men, women resisted the effects of a pro-inflammatory diet. Mediation analysis showed that white blood cell and neutrophil were mediators of the association between DII and all-cause mortality (p < 0.001). CONCLUSION: A pro-inflammatory diet is associated with all-cause mortality in adults with abdominal obesity and simple overweight or obesity, and this effect differs between men and women. Systemic inflammation may mediate the association between DII and all-cause mortality.

CT-based radiomics nomograms for preoperative prediction of diffuse-type and signet ring cell gastric cancer: a multicenter development and validation cohort.

BACKGROUND: The prevalence of diffuse-type gastric cancer (GC), especially signet ring cell carcinoma (SRCC), has shown an upward trend in the past decades. This study aimed to develop computed tomography (CT) based radiomics nomograms to distinguish diffuse-type and SRCC GC preoperatively. METHODS: A total of 693 GC patients from two centers were retrospectively analyzed and divided into training, internal validation and external validation cohorts. Radiomics features were extracted from CT images, and the Lauren radiomics model was established with a support vector machine (SVM) classifier to identify diffuse-type GC. The Lauren radiomics nomogram integrating radiomics features score (Rad-score) and clinicopathological characteristics were developed and evaluated regarding prediction ability. Further, the SRCC radiomics nomogram designed to identify SRCC from diffuse-type GC was developed and evaluated following the same procedures. RESULTS: Multivariate analysis revealed that Rad-scores was significantly associated with diffuse-type GC and SRCC (p < 0.001). The Lauren radiomics nomogram showed promising prediction performance with an area under the curve (AUC) of 0.895 (95%CI, 0.957-0.932), 0.841 (95%CI, 0.781-0.901) and 0.893 (95%CI, 0.831-0.955) in each cohort. The SRCC radiomics nomogram also showed good discrimination, with AUC of 0.905 (95%CI,0.866-0.944), 0.845 (95%CI, 0.775-0.915) and 0.918 (95%CI, 0.842-0.994) in each cohort. The radiomics nomograms showed great model fitness and clinical usefulness by calibration curve and decision curve analysis. CONCLUSION: Our CT-based radiomics nomograms had the ability to identify the diffuse-type and SRCC GC, providing a non-invasive, efficient and preoperative diagnosis method. They may help guide preoperative clinical decision-making and benefit GC patients in the future.

Thermally tunable and efficient second-harmonic generation on thin-film lithium niobate with integrated micro-heater.

In this Letter, we report thermo-optic tunable and efficient second-harmonic generation (SHG) based on an X-cut periodically poled lithium niobate (PPLN) waveguide. By applying an on-chip heater with thermo-isolation trenches and combining a type-0 quasi-phase matching mechanism, we experimentally achieve a high on-chip SHG conversion efficiency of 2500-3000% W-1 cm-2 and a large tuning power efficiency of 94 pm/mW inside a single 5-mm-long straight PPLN waveguide. Our design is for energy-efficient, high-performance nonlinear applications, such as wavelength conversion, highly tunable coherent light sources, and photon-pair generation.

Heterogeneously integrated III-V-on-lithium niobate broadband light sources and photodetectors.

Heterogeneous integration of III-V active devices on lithium niobate-on-insulator (LNOI) photonic circuits enable fully integrated transceivers. Here we present the co-integration of InP-based light-emitting diodes (LEDs) and photodetectors on an LNOI photonics platform. Both devices are realized based on the same III-V epitaxial layers stack adhesively bonded on an LNOI waveguide circuit. The light is evanescently coupled between the LNOI and III-V waveguide via a multiple-section adiabatic taper. The waveguide-coupled LEDs have a 3-dB bandwidth of 40 nm. The photodetector features a responsivity of 0.38 A/W in the 1550-nm wavelength range and a dark current of 9 nA at -0.5 V at room temperature.

Compact substrate-removed thin-film lithium niobate electro-optic modulator featuring polarization-insensitive operation.

A compact polarization-insensitive electro-optic (EO) modulator, which allows the laser and modulator to be located at different locations while using a standard single-mode fiber to interconnect them, is highly desirable for 5G or future 6G wireless networks. Herein, we propose a modulator based on substrate-removed thin-film lithium niobate. The proposed device exhibits a polarization-dependent loss of 0.35 dB and on-chip loss of approximately 2 dB. The polarization insensitivity of the proposed device was experimentally demonstrated using a four-level pulse-amplitude modulation format at 50 Gbaud (100 Gb/ s).

A novel inflammation-nutrition biomarker score for predicting prognosis of patients with cancer: results from a multicenter study.

BACKGROUND: This study aimed to develop an innovative inflammation-nutrition biomarker score (INS) system to stratify the prognoses of patients with cancer. METHODS: A total of 5,221 patients with cancer from multiple centers in China between June 2010 and December 2017 were enrolled in this prospective cohort study. We compared the commonly used inflammation and nutrition biomarkers and selected the most valuable to develop the novel INS system. Survival curves were assessed using the Kaplan-Meier method and the log-rank test to evaluate the difference in survival rates between groups. The Cox proportional hazards model was used to investigate the association between biomarkers and all-cause mortality. RESULTS: As the risk stratification of INS increased (1 to 5), the rate of death for cancer patients gradually increased (25.43% vs. 37.09% vs. 44.59% vs. 56.21% vs. 61.65%, p < 0.001). The INS system was associated with all-cause mortality in patients with cancer. Patients with both high inflammation and nutrition risk (INS = 5) were estimated to have much worse prognosis than those with neither (HR, 2.606; 95%CI, 2.261-3.003, p < 0.001). Subsequently, the results of randomized internal validation also confirmed that INS system had an ideal effect in identifying adverse outcomes. In addition, the INS system could be used as a supplement to pathological stages in prognosis assessment, and had a higher predictive value in comparison with the constitute biomarkers. Patients with a high INS had less functional ability, reduced quality of life, and were at high risk of malnutrition, cachexia, and poor short-term outcomes. CONCLUSION: The INS system based on inflammation and nutrition biomarkers is a simple and effective prognostic stratification tool for patients with cancer, which can provide a valuable reference for clinical prognosis assessment and treatment strategy formulation.

Lymphocyte to C-reactive protein ratio could better predict the prognosis of patients with stage IV cancer.

BACKGROUND: Systemic inflammation is currently regarded as a hallmark of cancer. This study aimed to accurately clarify the prognostic value of various inflammatory markers in patients with stage IV cancer. METHODS: This study assessed 2,424 patients with cancer diagnosed with cancer in tumor, node, metastasis (TNM) stage IV. After evaluating the predictive value of 13 inflammatory indicators for patient prognosis using the C index, the lymphocyte C-reactive protein ratio (LCR) was selected to elucidate the prognostic and predictive values in patients with stage IV cancer. Kaplan-Meier and Cox proportional hazards regression models were used to analyze long-term survival. RESULTS: A total of 1,457 men (60.1%) and 967 women (39.9%) diagnosed with TNM stage IV cancer were enrolled. A ratio of 2,814 was defined as the optimal cut-off value for the LCR. The LCR was the most accurate prognosis predictor for patients with stage IV cancer among the 13 inflammatory nutritional markers evaluated. The multivariate-adjusted restricted cubic spline plot suggested that LCR had an L-shaped dose-response association with all-cause mortality risk. Patients with lower LCR levels tended to present with worse prognoses. Kaplan-Meier curves and log-rank test results showed that the high LCR groups (LCR ≥ 2,814) exhibited a better prognosis, whereas patients with stage IV cancer of different sex and tumor types (for example, gastrointestinal tumor, non-gastrointestinal tumor, and lung cancer) had a worse survival time. CONCLUSION: The LCR score can be regarded as a stable and useful biomarker to predict prognosis in patients with TNM stage IV compared to other evaluated inflammation indicators.

Comprehensive comparison of the prognostic value of systemic inflammation biomarkers for cancer cachexia: a multicenter prospective study.

AIMS: Systemic inflammation plays an important role in cancer cachexia. However, among the systemic inflammatory biomarkers, it is unclear which has optimal prognostic value for cancer cachexia. METHODS: The Kaplan-Meier method was used and the log-rank analysis was performed to estimate survival differences between groups. Cox proportional hazard regression analyses were conducted to assess independent risk factors for all-cause mortality. RESULTS: The C-reactive protein-to-albumin ratio (CAR) was the optimal prognostic assessment tool for patients with cancer cachexia, with 1-, 3-, and 5-year predictive powers of 0.650, 0.658, and 0.605, respectively. Patients with a high CAR had significantly lower survival rates than those with a low CAR. Moreover, CAR can differentiate the prognoses of patients with the same pathological stage. Cox proportional risk regression analyses showed that a high CAR was an independent risk factor for cancer cachexia. For every standard deviation increase in CAR, the risk of poor prognosis for patients with cancer cachexia was increased by 20% (hazard ratio = 1.200, 95% confidence interval = 1.132-1.273, P < 0.001). CONCLUSIONS: CAR is an effective representative of systemic inflammation and a powerful factor for predicting the life function and clinical outcome of patients with cancer cachexia.

The impact of crowd gatherings on the spread of COVID-19.

Crowd gatherings are an important cause of COVID-19 outbreaks. However, how the scale, scene and other factors of gatherings affect the spread of the epidemic remains unclear. A total of 184 gathering events worldwide were collected to construct a database, and 99 of them with a clear gathering scale were used for statistical analysis of the impact of these factors on the disease incidence among the crowd in the study. The results showed that the impact of small-scale (less than 100 people) gathering events on the spread of COVID-19 in the city is also not to be underestimated due to their characteristics of more frequent occurrence and less detection and control. In our dataset, 22.22% of small-scale events have an incidence of more than 0.8. In contrast, the incidence of most large-scale events is less than 0.4. Gathering scenes such as "Meal" and "Family" occur in densely populated private or small public places have the highest incidence. We further designed a model of epidemic transmission triggered by crowd gathering events and simulated the impact of crowd gathering events on the overall epidemic situation in the city. The simulation results showed that the number of patients will be drastically reduced if the scale and the density of crowds gathering are halved. It indicated that crowd gatherings should be strictly controlled on a small scale. In addition, it showed that the model well reproduce the epidemic spread after crowd gathering events better than does the original SIER model and could be applied to epidemic prediction after sudden gathering events.

Comparison of PM2.5 and CO2 Concentrations in Large Cities of China during the COVID-19 Lockdown.

Estimating the impacts on PM2.5 pollution and CO2 emissions by human activities in different urban regions is important for developing efficient policies. In early 2020, China implemented a lockdown policy to contain the spread of COVID-19, resulting in a significant reduction of human activities. This event presents a convenient opportunity to study the impact of human activities in the transportation and industrial sectors on air pollution. Here, we investigate the variations in air quality attributed to the COVID-19 lockdown policy in the megacities of China by combining in-situ environmental and meteorological datasets, the Suomi-NPP/VIIRS and the CO2 emissions from the Carbon Monitor project. Our study shows that PM2.5 concentrations in the spring of 2020 decreased by 41.87% in the Yangtze River Delta (YRD) and 43.30% in the Pearl River Delta (PRD), respectively, owing to the significant shutdown of traffic and manufacturing industries. However, PM2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region only decreased by 2.01% because the energy and steel industries were not fully paused. In addition, unfavorable weather conditions contributed to further increases in the PM2.5 concentration. Furthermore, CO2 concentrations were not significantly affected in China during the short-term emission reduction, despite a 19.52% reduction in CO2 emissions compared to the same period in 2019. Our results suggest that concerted efforts from different emission sectors and effective long-term emission reduction strategies are necessary to control air pollution and CO2 emissions.