In the United States (US), the Surveillance, Epidemiology, and End Results (SEER) program is the only comprehensive source of population-based information that includes stage of cancer at the time of diagnosis and patient survival data. This program aims to provide a database about cancer incidence and survival for studies of surveillance and the development of analytical and methodological tools in the cancer field. Currently, the SEER program covers approximately half of the total cancer patients in the US. A growing number of clinical studies have applied the SEER database in various aspects. However, the intrinsic features of the SEER database, such as the huge data volume and complexity of data types, have hindered its application. In this review, we provided a systematic overview of the commonly used methodologies and study designs for retrospective epidemiological research in order to illustrate the application of the SEER database. Therefore, the goal of this review is to assist researchers in the selection of appropriate methods and study designs for enhancing the robustness and reliability of clinical studies by mining the SEER database.

Physiological and biochemical processes across tissues of the body are regulated in response to the high demands of intense physical activity in several occupations, such as firefighting, law enforcement, military, and sports. A better understanding of such processes can ultimately help improve human performance and prevent illnesses in the work environment.

To study regulatory processes in intense physical activity simulating real-life conditions, we performed a multi-omics analysis of 3 biofluids (blood plasma, urine, and saliva) collected from 11 wildland firefighters before and after a 45 min, intense exercise regimen. Omics profiles post- vs. pre-exercise were compared by Student’s t-test followed by pathway analysis and comparison between the different omics modalities.

Our multi-omics analysis identified and quantified 3835 proteins, 730 lipids and 182 metabolites combining the 3 different types of samples. The blood plasma analysis revealed signatures of tissue damage and acute repair response accompanied by enhanced carbon metabolism to meet energy demands. The urine analysis showed a strong, concomitant regulation of 6 out of 8 identified proteins from the renin-angiotensin system supporting increased excretion of catabolites, reabsorption of nutrients and maintenance of fluid balance. In saliva, we observed a decrease in 3 pro-inflammatory cytokines and an increase in 8 antimicrobial peptides. A systematic literature review identified 6 papers that support an altered susceptibility to respiratory infection.

This study shows simultaneous regulatory signatures in biofluids indicative of homeostatic maintenance during intense physical activity with possible effects on increased infection susceptibility, suggesting that caution against respiratory diseases could benefit workers on highly physical demanding jobs.

Neutrophils are traditionally viewed as first responders but have a short onset of action in response to traumatic brain injury (TBI). However, the heterogeneity, multifunctionality, and time-dependent modulation of brain damage and outcome mediated by neutrophils after TBI remain poorly understood.

Using the combined single-cell transcriptomics, metabolomics, and proteomics analysis from TBI patients and the TBI mouse model, we investigate a novel neutrophil phenotype and its associated effects on TBI outcome by neurological deficit scoring and behavioral tests. We also characterized the underlying mechanisms both in vitro and in vivo through molecular simulations, signaling detections, gene expression regulation assessments [including dual-luciferase reporter and chromatin immunoprecipitation (ChIP) assays], primary cultures or co-cultures of neutrophils and oligodendrocytes, intracellular iron, and lipid hydroperoxide concentration measurements, as well as forkhead box protein O1 (FOXO1) conditional knockout mice.

We identified that high expression of the FOXO1 protein was induced in neutrophils after TBI both in TBI patients and the TBI mouse model. Infiltration of these FOXO1^high neutrophils in the brain was detected not only in the acute phase but also in the chronic phase post-TBI, aggravating acute brain inflammatory damage and promoting late TBI-induced depression. In the acute stage, FOXO1 upregulated cytoplasmic Versican (VCAN) to interact with the apoptosis regulator B-cell lymphoma-2 (BCL-2)-associated X protein (BAX), suppressing the mitochondrial translocation of BAX, which mediated the antiapoptotic effect companied with enhancing interleukin-6 (IL-6) production of FOXO1^high neutrophils. In the chronic stage, the "FOXO1-transferrin receptor (TFRC)" mechanism contributes to FOXO1^high neutrophil ferroptosis, disturbing the iron homeostasis of oligodendrocytes and inducing a reduction in myelin basic protein, which contributes to the progression of late depression after TBI.

FOXO1^high neutrophils represent a novel neutrophil phenotype that emerges in response to acute and chronic TBI, which provides insight into the heterogeneity, reprogramming activity, and versatility of neutrophils in TBI.

Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis.

Climate change profoundly shapes the population health at the global scale. However, there was still insufficient and inconsistent evidence for the association between heat exposure and chronic kidney disease (CKD).

In the present study, we studied the association of heat exposure with hospitalizations for cause-specific CKD using a national inpatient database in China during the study period of hot season from 2015 to 2018. Standard time-series regression models and random-effects Meta-analysis were developed to estimate the city-specific and national averaged associations at a 7 lag-day span, respectively.

A total of 768,129 hospitalizations for CKD was recorded during the study period. The results showed that higher temperature was associated with elevated risk of hospitalizations for CKD, especially in sub-tropical cities. With a 1 ℃ increase in daily mean temperature, the cumulative relative risks (RR) over lag 0–7 d were 1.008 [95% confidence interval (CI) 1.003–1.012] for nationwide. The attributable fraction of CKD hospitalizations due to high temperatures was 5.50%. Stronger associations were observed among younger patients and those with obstructive nephropathy. Our study also found that exposure to heatwaves was associated with added risk of hospitalizations for CKD compared to non-heatwave days (RR=1.116, 95%CI 1.069–1.166) above the effect of daily mean temperature.

Short-term heat exposure may increase the risk of hospitalization for CKD. Our findings provide insights into the health effects of climate change and suggest the necessity of guided protection strategies against the adverse effects of high temperatures.