An unexpected role of neutrophils in clearing apoptotic hepatocytes in vivo.

Billions of apoptotic cells are removed daily in a human adult by professional phagocytes (e.g. macrophages) and neighboring nonprofessional phagocytes (e.g. stromal cells). Despite being a type of professional phagocyte, neutrophils are thought to be excluded from apoptotic sites to avoid tissue inflammation. Here, we report a fundamental and unexpected role of neutrophils as the predominant phagocyte responsible for the clearance of apoptotic hepatic cells in the steady state. In contrast to the engulfment of dead cells by macrophages, neutrophils burrowed directly into apoptotic hepatocytes, a process we term perforocytosis, and ingested the effete cells from the inside. The depletion of neutrophils caused defective removal of apoptotic bodies, induced tissue injury in the mouse liver, and led to the generation of autoantibodies. Human autoimmune liver disease showed similar defects in the neutrophil-mediated clearance of apoptotic hepatic cells. Hence, neutrophils possess a specialized immunologically silent mechanism for the clearance of apoptotic hepatocytes through perforocytosis, and defects in this key housekeeping function of neutrophils contribute to the genesis of autoimmune liver disease.

Every day, the immune cells clears the remains of billions of old and damaged cells that have undergone a controlled form of death. Removing them quickly helps to prevent inflammation or the development of autoimmune diseases. While immune cells called neutrophils are generally tasked with removing invading bacteria, macrophages are thought to be responsible for clearing dead cells. However, in healthy tissue, the process occurs so efficiently that it can be difficult to confirm which cells are responsible. To take a closer look, Cao et al. focused on the liver by staining human samples to identify both immune and dead cells. Unexpectedly, there were large numbers of neutrophils visible inside dead liver cells. Further experiments in mice revealed that after entering the dead cells, neutrophils engulfed the contents and digested the dead cell from the inside out. This was a surprising finding because not only are neutrophils not usually associated with dead cells, but immune cells usually engulf cells and bacteria from the outside rather than burrowing inside them. The importance of this neutrophil behaviour was shown when Cao et al. studied samples from patients with an autoimmune disease where immune cells attack the liver. In this case, very few dead liver cells contained neutrophils, and the neutrophils themselves did not seem capable of removing the dead cells, leading to inflammation. This suggests that defective neutrophil function could be a key contributor to this autoimmune disease. The findings identify a new role for neutrophils in maintaining healthy functioning of the liver and reveal a new target in the treatment of autoimmune diseases. In the future, Cao et al. plan to explore whether compounds that enhance clearance of dead cells by neutrophils can be used to treat autoimmune liver disease in mouse models of the disease.

Global, regional, and national time trends in mortality for stroke, 1990-2019: An age-period-cohort analysis for the global burden of disease 2019 study and implications for stroke prevention.

BACKGROUND: Despite the fact that stroke is the second leading cause of death globally, a comprehensive and comparable assessment of mortality, and epidemiologic trends has not been conducted for most regions.We estimated the global and regional burden of stroke from 1990 to 2019 using data from the 2019 Global Study of Diseases, Injuries, and Risk Factors. METHODS: For the period between 1990 and 2019, we used an age-period-cohort model to calculate the annual percentage changes in mortality (net drifts), local drifts, and period and cohort relative risks (period/cohort effects). Meanwhile, to quantify the temporal trends in stroke age-standardised mortality rate (ASMR), Average annual percentage changes (AAPCs) were determined by sex, area. With the potential to uncover disparities and treatment gaps in stroke care, this approach enables the examination and differentiation of age, period, and cohort effects in mortality trends. FINDINGS: Global stroke deaths in 2019 were 6,552,725 (95% UI 5,995,200 to 7,015,139). Between 1990 and 2019, the ASMR declined globally by 36.43% (95% UI -41.65 to -31.2), with decreases in all SDI quintiles. The net drift in stroke mortality from 1990 to 2019 varied from -2.83% per year (95% confidence interval [CI]:-3.39 to -2.77) in countries with a high Socio-demographic Index (SDI) to -1.21% per year (95% CI: -1.26 to -1.16) in countries with a low SDI. During the past 30 years, favorable mortality reductions were generally found in high-SDI countries (net drift = -3.1% [95% CI: -3.4 to -2.8] per year) and high-middle SDI countries (-2.8% [-3.0 to -2.6]). However, 31 of 204 countries had either increasing trends (net drifts≥0.0%) or stagnated reductions (≥ - 0.5%) in mortality. The relative risk of mortality generally showed improving trends over time and in successively younger birth cohorts among high and high-middle SDI countries, with the exceptions of Kuwait, Ukraine, Kazakhstan, Guam, RussianFederation, Lithuania, Turkey, Montenegro, Serbia, Bosnia and Herzegovin, and Bulgaria. INTERPRETATION: Notwithstanding mortality from stroke has increased globally over the past 30 years, adverse period and cohort effects have been found in many countries, calling into question the adequacy of healthcare for stroke patients of all ages. These lapses have a significant impact on the likelihood of achieving the Sustainable Development Goal (SDG) targets on mortality from age 60+ and NCDs.

Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases.

Background: Heart failure (HF) combined with hypertension is an extremely important cause of in-hospital mortality, especially for the intensive care unit (ICU) patients. However, under intense working pressure, the medical staff are easily overwhelmed by the large number of clinical signals generated in the ICU, which may lead to treatment delay, sub-optimal care, or even wrong clinical decisions. Individual risk stratification is an essential strategy for managing ICU patients with HF combined with hypertension. Artificial intelligence, especially machine learning (ML), can develop superior models to predict the prognosis of these patients. This study aimed to develop a machine learning method to predict the 28-day mortality for ICU patients with HF combined with hypertension. Methods: We enrolled all critically ill patients with HF combined with hypertension in the Medical Information Mart for IntensiveCare Database-IV (MIMIC-IV, v.1.4) and the eICU Collaborative Research Database (eICU-CRD) from 2008 to 2019. Subsequently, MIMIC-IV was divided into training cohort and testing cohort in an 8:2 ratio, and eICU-CRD was designated as the external validation cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression with internal tenfold cross-validation was used for data dimension reduction and identifying the most valuable predictive features for 28-day mortality. Based on its accuracy and area under the curve (AUC), the best model in the validation cohort was selected. In addition, we utilized the Shapley Additive Explanations (SHAP) method to highlight the importance of model features, analyze the impact of individual features on model output, and visualize an individual's Shapley values. Results: A total of 3,458 and 6582 patients with HF combined with hypertension in MIMIC-IV and eICU-CRD were included. The patients, including 1,756 males, had a median (Q1, Q3) age of 75 (65, 84) years. After selection, 22 out of a total of 58 clinical parameters were extracted to develop the machine-learning models. Among four constructed models, the Neural Networks (NN) model performed the best predictive performance with an AUC of 0.764 and 0.674 in the test cohort and external validation cohort, respectively. In addition, a simplified model including seven variables was built based on NN, which also had good predictive performance (AUC: 0.741). Feature importance analysis showed that age, mechanical ventilation (MECHVENT), chloride, bun, anion gap, paraplegia, rdw (RDW), hyperlipidemia, peripheral capillary oxygen saturation (SpO2), respiratory rate, cerebrovascular disease, heart rate, white blood cell (WBC), international normalized ratio (INR), mean corpuscular hemoglobin concentration (MCHC), glucose, AIDS, mean corpuscular volume (MCV), N-terminal pro-brain natriuretic peptide (Npro. BNP), calcium, renal replacement therapy (RRT), and partial thromboplastin time (PTT) were the top 22 features of the NN model with the greatest impact. Finally, after hyperparameter optimization, SHAP plots were employed to make the NN-based model interpretable with an analytical description of how the constructed model visualizes the prediction of death. Conclusion: We developed a predictive model to predict the 28-day mortality for ICU patients with HF combined with hypertension, which proved superior to the traditional logistic regression analysis. The SHAP method enables machine learning models to be more interpretable, thereby helping clinicians to better understand the reasoning behind the outcome and assess in-hospital outcomes for critically ill patients.

A nomogram for prediction of distant metastasis in patients with hypopharyngeal squamous cell carcinoma: a study based on the SEER database.

BACKGROUND: The prognosis of hypopharyngeal squamous cell carcinoma (HPSCC) is poor due to its high incidence of local invasion and distant metastasis (DM). This study aims to explore the DM risk factors of HPSCC and establish a clinical prediction model. METHODS: We downloaded patient data from the Public Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2018. Univariate and multivariate logistic regression analyses were performed to screen the clinical risk factors for DM of HPSCC. A new nomogram prediction model was then established based on the selected clinical risk factors. We further validated the model's accuracy based on the concordance index (C-index), the area under the receiver operating characteristic (AUC) curve, and the calibration plot. The decision curve analysis (DCA) to test the potential clinical value of the new model was also applied. RESULTS: A total of 3502 patients were enrolled; the patients with HPSCC were randomly assigned to a training set (n=2463) and a validation set (n=1039). Multivariate Logistic model analysis suggested that sex, T stage, N stage, and the total number of tumors were influence factors for DM of HPSCC. We established and validated a novel nomogram prediction model based on the multivariate logistic model with these influence factors. The C-index was 0.943 and 0.849 in the training and validation sets respectively. The AUC of the training set was 0.705 (95% CI: 0.669-0.741), and the validation set was 0.667 (95% CI: 0.609-0.725). The calibration plot shows that the actual observation value was similar to the predicted value, meaning the model has an excellent discrimination ability. DCA of the nomogram in the training and validation sets suggested that our nomogram has potential application value. CONCLUSIONS: We found that sex, T stage, N stage, and the total number of tumors are independent risk factors for DM of HPSCC. We developed a novel prediction model to predict DM in patients with HPSCC. This nomogram can identify patients with a high risk of DM and has a high clinical application value.

A Visualized Dynamic Prediction Model for Overall Survival in Elderly Patients With Pancreatic Cancer for Smart Medical Services.

Background: Pancreatic cancer (PC) is a highly malignant tumor of the digestive system. The number of elderly patients with PC is increasing, and older age is related to a worse prognosis. Accurate prognostication is crucial in treatment decisions made for people diagnosed with PC. However, an accurate predictive model for the prognosis of these patients is still lacking. We aimed to construct nomograms for predicting the overall survival (OS) of elderly patients with PC. Methods: Patients with PC, older than 65 years old from 2010 to 2015 in the Surveillance, Epidemiology, and End Results database, were selected and randomly divided into training cohort (n = 4,586) and validation cohort (n = 1,966). Data of patients in 2016-2018 (n = 1,761) were used for external validation. Univariable and forward stepwise multivariable Cox analysis was used to determine the independent prognostic factors. We used significant variables in the training set to construct nomograms predicting prognosis. The performance of the models was evaluated for their discrimination and calibration power based on the concordance index (C-index), calibration curve, and the decision curve analysis (DCA). Results: Age, insurance, grade, surgery, radiation, chemotherapy, T, N, and American Joint Commission on Cancer were independent predictors for OS and thus were included in our nomogram. In the training cohort and validation cohort, the C-indices of our nomogram were 0.725 (95%CI: 0.715-0.735) and 0.711 (95%CI: 0.695-0.727), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves showed similar results. The calibration curves showed a high consensus between observations and predictions. In the external validation cohort, C-index (0.797, 95%CI: 0.778-0.816) and calibration curves also revealed high consistency between observations and predictions. The nomogram-related DCA curves showed better clinical utility compared to tumor-node-metastasis staging. In addition, we have developed an online prediction tool for OS. Conclusions: A web-based prediction model for OS in elderly patients with PC was constructed and validated, which may be useful for prognostic assessment, treatment strategy selection, and follow-up management of these patients.

Analysis of Bacterial Biofilm Formation in Patients with Malignancy Undergoing Double J Stent Indwelling and Its Influencing Factors.

OBJECTIVE: To analyze the bacterial biofilm (BF) formation in patients with malignancy undergoing double J stent indwelling and its influencing factors. METHODS: A total of 167 patients with malignant tumors who received double J stent indwelling in the hospital from January 2018 to January 2021 were included in the study. The urine and double J stent samples were collected for bacterial identification and observed for BF formation on the surface of the urinary catheter under a scanning electron microscope (SEM). Univariate and multivariate logistic regression analyses were used to analyze the influencing factors of BF. RESULTS: The BF formation rate was 34.73% (58/167). The BF formation rate of positive specimens cultured in urine and double J stent was significantly higher than that of negative ones (P<0.05). Staphylococcus was the main BF bacteria in double J stent and urine culture specimens, followed by Enterococcus, Pseudomonas, Enterobacter, and Acinetobacter. Compared with the non-BF group, the number of viable bacteria in the double J stent and urine and the catheterization time in the BF group rose markedly (P<0.05). Advanced age, chemotherapy, anemia, indwelling time ≥90d, and urinary tract infection were risk factors for BF formation in patients with malignancy undergoing double J stent indwelling (P<0.05). CONCLUSION: There is a high rate of BF formation in patients with malignancy undergoing double J stent indwelling, with Staphylococcus as the dominant species. Treatment requires enhanced urinary catheter management and nutritional status to inhibit BF formation and lower the rate of urinary catheter-related infections.