Graphene is an emerging nanomaterial increasingly being used in electrochemical biosensing applications owing to its high surface area, excellent conductivity, ease of functionalization, and superior electrocatalytic properties compared to other carbon-based electrodes and nanomaterials, enabling faster electron transfer kinetics and higher sensitivity. Graphene electrochemical biosensors may have the potential to enable the rapid, sensitive, and low-cost detection of cancer biomarkers. This paper reviews early-stage research and proof-of-concept studies on the development of graphene electrochemical biosensors for potential future cancer diagnostic applications. Various graphene synthesis methods are outlined along with common functionalization approaches using polymers, biomolecules, nanomaterials, and synthetic chemistry to facilitate the immobilization of recognition elements and improve performance. Major sensor configurations including graphene field-effect transistors, graphene modified electrodes and nanocomposites, and 3D graphene networks are highlighted along with their principles of operation, advantages, and biosensing capabilities. Strategies for the immobilization of biorecognition elements like antibodies, aptamers, peptides, and DNA/RNA probes onto graphene platforms to impart target specificity are summarized. The use of nanomaterial labels, hybrid nanocomposites with graphene, and chemical modification for signal enhancement are also discussed. Examples are provided to illustrate applications for the sensitive electrochemical detection of a broad range of cancer biomarkers including proteins, circulating tumor cells, DNA mutations, non-coding RNAs like miRNA, metabolites, and glycoproteins. Current challenges and future opportunities are elucidated to guide ongoing efforts towards transitioning graphene biosensors from promising research lab tools into mainstream clinical practice. Continued research addressing issues with reproducibility, stability, selectivity, integration, clinical validation, and regulatory approval could enable wider adoption. Overall, graphene electrochemical biosensors present powerful and versatile platforms for cancer diagnosis at the point of care.

Background: Ischemic heart disease (IHD) is the leading cause of death among noncommunicable diseases worldwide, but data on current epidemiological patterns and associated risk factors are lacking. Objective: This study assessed the global, regional, and national trends in IHD mortality and attributable risks since 1990. Methods: Mortality data were obtained from the Global Burden of Disease 2019 Study. We used an age-period-cohort model to calculate longitudinal age curves (expected longitudinal age-specific rate), net drift (overall annual percentage change), and local drift (annual percentage change in each age group) from 15 to >95 years of age and estimate cohort and period effects between 1990 and 2019. Deaths from IHD attributable to each risk factor were estimated on the basis of risk exposure, relative risks, and theoretical minimum risk exposure level. Results: IHD is the leading cause of death in noncommunicable disease–related mortality (118.1/598.8, 19.7%). However, the age-standardized mortality rate for IHD decreased by 30.8% (95% CI –34.83% to –27.17%) over the past 30 years, and its net drift ranged from –2.89% (95% CI –3.07% to –2.71%) in high sociodemographic index (SDI) region to –0.24% (95% CI –0.32% to –0.16%) in low-middle–SDI region. The greatest decrease in IHD mortality occurred in the Republic of Korea (high SDI) with net drift –6.06% (95% CI –6.23% to –5.88%), followed by 5 high-SDI nations (Denmark, Norway, Estonia, the Netherlands, and Ireland) and 2 high-middle–SDI nations (Israel and Bahrain) with net drift less than –5.00%. Globally, age groups of >60 years continued to have the largest proportion of IHD-related mortality, with slightly higher mortality in male than female group. For period and birth cohort effects, the trend of rate ratios for IHD mortality declined across successive period groups from 2000 to 2004 and birth cohort groups from 1985 to 2000, with noticeable improvements in high-SDI regions. In low-SDI regions, IHD mortality significantly declined in female group but fluctuated in male group across successive periods; sex differences were greater in those born after 1945 in middle- and low-middle–SDI regions and after 1970 in low-SDI regions. Metabolic risks were the leading cause of mortality from IHD worldwide in 2019. Moreover, smoking, particulate matter pollution, and dietary risks were also important risk factors, increasingly occurring at a younger age. Diets low in whole grains and legumes were prominent dietary risks in both male and female groups, and smoking and high-sodium diet mainly affect male group. Conclusions: IHD, a major concern, needs focused health care attention, especially for older male individuals and those in low-SDI regions. Metabolic risks should be prioritized for prevention, and behavioral and environmental risks should attract more attention to decrease IHD mortality.

Background: systemic inflammation disorders were observed in chronic kidney disease (CKD). Whether the systemic inflammatory indicators could be optimal predictors for the survival of CKD remains less studied.Methods: In this study, participants were selected from the datasets of the National Health and Nutrition Examination Survey (NHANES) between 1999 to 2018 years. Four systemic inflammatory indicators were evaluated by the peripheral blood tests including systemic immune-inflammation index (SII, platelet*neutrophil/lymphocyte), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR). Kaplan-Meier curves, restricted cubic spline (RCS), and Cox regression analysis were used to evaluate the association between the inflammatory index with the all-cause mortality of CKD. Receiver operating characteristic (ROC) and concordance index (C-index) were used to determine the predictive accuracy of varied systemic inflammatory indicators. Sensitive analyses were conducted to validate the robustness of the main findings.Results: A total of 6,880 participants were included in this study. The mean age was 67.03 years old. Among the study population, the mean levels of systemic inflammatory indicators were 588.35 in SII, 2.45 in NLR, 133.85 in PLR, and 3.76 in LMR, respectively. The systemic inflammatory indicators of SII, NLR, and PLR were all significantly positively associated with the all-cause mortality of CKD patients, whereas the high value of LMR played a protectable role in CKD patients. NLR and LMR were the leading predictors in the survival of CKD patients [Hazard ratio (HR) =1.21, 95% confidence interval (CI): 1.07-1.36, p = 0.003 (3^rd quartile), HR = 1.52, 95%CI: 1.35-1.72, p<0.001 (4^th quartile) in NLR, and HR = 0.83, 95%CI: 0.75-0.92, p<0.001 (2^nd quartile), HR = 0.73, 95%CI: 0.65-0.82, p<0.001 (3^rd quartile), and = 0.74, 95%CI: 0.65-0.83, p<0.001 (4^th quartile) in LMR], with a C-index of 0.612 and 0.624, respectively. The RCS curves showed non-linearity between systemic inflammatory indicators and all-cause mortality risk of the CKD population.Conclusion: Our study highlights that systemic inflammatory indicators are important for predicting the survival of the U.S. population with CKD. The systemic inflammatory indicators would add additional clinical value to the health care of the CKD population.

Respiratory tract cancers emerged as a public health challenge with the highest incidence and mortality among all cancer types in China, despite many national policies in place, such as early cancer screening. It is of outmost importance to monitor the burden of respiratory tract cancers across China and its provinces for refining health strategies. Based on Global Burden of Disease (GBD) estimates, the present study investigated the age-sex specific pattern alterations of incidence, prevalence, mortality, and disability adjusted life years (DALYs) of respiratory tract cancers in China from 1990 to 2021, as well as its Estimated Annual Percentage Change (EAPC), Age-Standardized Incidence Rate (ASIR), and Age-Standardized Mortality Rate (ASMR). Between 1990 and 2021, China experienced an escalation in burdens of respiratory tract cancers, with the new cases surging from 274,752 (95% Uncertainty Interval (UI): 234,741–315,112) to 934,704 (750,040–1,136,938), marking an increase of 240.20% (156.05–342.29). Their attributed deaths similarly increased from 278,235 (238,518–322,013) to 814,121 (652,231–994,858). In 2021, the eastern and northeastern regions reported the highest incidence and mortality rates, particularly Shandong, with the highest new cases at 77,225 (58,842–101,352), while Tibet, Qinghai, and Macau observed the lowest. Regarding laryngeal cancer, Guangdong reported the highest incidence at 3466 (2230–4934), with Hainan exhibiting the highest ASIR at 3.46 (2.10–5.11) per 100,000 person-years and ASMR at 2.11 (1.37–3.09) per 100,000 person-years. Over the same timeframe, the EAPC for the ASIR of tracheal, bronchus, and lung cancer was 0.88 (0.63–1.14), and for ASMR, it was 0.29 (0.05–0.62), signifying an upward trend. Conversely, laryngeal cancer exhibited a stable ASIR with an EAPC of 0.04 (−0.22 to 0.30) and a declining ASMR with an EAPC of −1.69 (−1.80 to 1.59). Tracheal, bronchus, and lung cancer burdens exhibited notable sex differences, with their ASIR being 62.63 (46.50–79.90) per 100,000 person-years in males and 28.16 (22.22–34.90) per 100,000 person-years in females. For laryngeal cancer, the ASIR was 3.12 (2.34–4.04) per 100,000 person-years for males and 0.58 (0.35–0.79) per 100,000 person-years for females. Smoking and air pollution emerged as the predominant risk factors contributed to tracheal, bronchus, and lung cancer, accounting for 61.58% (30.00–82.95) and 25.98% (16.94–35.00) of deaths, respectively. In contrast, smoking contributed more to laryngeal cancer-caused deaths (76.70% [65.55–85.15]), followed by alcohol use (14.52% [7.70–20.99]). The burden of respiratory tract cancers in China has increased over the past three decades, and without intervention, the associated health losses could escalate further. This burden predominantly affected the eastern provinces, particularly impacting older males. Our findings advocate for the formulation of targeted prevention, screening, and intervention strategies based on regional and sex disparities. 10.13039/100000865 Bill & Melinda Gates Foundation .

We aimed to develop a machine-learning based predictive model to identify 30-day readmission risk in Acute heart failure (AHF) patients. In this study 2232 patients hospitalized with AHF were included. The variance inflation factor value and 5-fold cross-validation were used to select vital clinical variables. Five machine learning algorithms with good performance were applied to develop models, and the discrimination ability was comprehensively evaluated by sensitivity, specificity, and area under the ROC curve (AUC). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values. Finally, the XGBoost model performs optimally: the greatest AUC of 0.763 (0.703–0.824), highest sensitivity of 0.660, and high accuracy of 0.709. This study developed an optimal XGBoost model to predict the risk of 30-day unplanned readmission for AHF patients, which showed more significant performance compared with traditional logistic regression (LR) model.