An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field.

OBJECTIVE: The objective of this study was to investigate the association between inflammatory burden index (IBI) and all-cause mortality in the general population aged over 45 years.
RESULTS: The study included 8827 participants from the National Health and Examination Nutrition Survey (NHANES) who were aged over 45 years. The IBI was calculated using three markers: C-reaction protein × neutrophil/lymphocyte, and all the participants were classified into four groups (Quartile 1: IBI ≤0.178, N = 2206; Quartile 2: 0.178 1.099, 2207). Cox proportional hazards regression models were used to estimate hazard ratios (HR) and 95 % confidence interval (CI) for the association between IBI and all-cause mortality. During a median follow-up of 129 month, 2431 deaths occurred. The all-cause mortality rate in Quartile 1, Quartile 2, Quartile 3 and Quartile 4 was 14.76 %, 17.67 %, 23.18 % and 29.69 %, respectively (p < 0.001). After adjustment for demographic, and potential clinical factors, higher IBI was significantly associated with an increased risk of all-cause mortality (Quartile 3 vs. Quartile 1: HR = 1.26, 95 % CI: 1.08 to 1.46, p = 0.003; Quartile 4 vs. Quartile 1: HR = 1.59, 95 % CI: 1.40 to 1.80, p < 0.001). Furthermore, the results of the restricted cubic spline analysis suggested that the association between IBI and all-cause mortality was nonlinear and positive, without specific threshold value.
CONCLUSIONS: This study supports that higher IBI is associated with an increased risk of all-cause mortality in the general population aged over 45 years.

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Artificial Intelligence (AI) technology has been developed significantly in recent years. The fairness of medical AI is of great concern due to its direct relation to human life and health. This review aims to analyze the existing research literature on fairness in medical AI from the perspectives of computer science, medical science, and social science (including law and ethics). The objective of the review is to examine the similarities and differences in the understanding of fairness, explore influencing factors, and investigate potential measures to implement fairness in medical AI across English and Chinese literature.
This study employed a scoping review methodology and selected the following databases: Web of Science, MEDLINE, Pubmed, OVID, CNKI, WANFANG Data, etc., for the fairness issues in medical AI through February 2023. The search was conducted using various keywords such as \"artificial intelligence,\" \"machine learning,\" \"medical,\" \"algorithm,\" \"fairness,\" \"decision-making,\" and \"bias.\" The collected data were charted, synthesized, and subjected to descriptive and thematic analysis.
After reviewing 468 English papers and 356 Chinese papers, 53 and 42 were included in the final analysis. Our results show the three different disciplines all show significant differences in the research on the core issues. Data is the foundation that affects medical AI fairness in addition to algorithmic bias and human bias. Legal, ethical, and technological measures all promote the implementation of medical AI fairness.
Our review indicates a consensus regarding the importance of data fairness as the foundation for achieving fairness in medical AI across multidisciplinary perspectives. However, there are substantial discrepancies in core aspects such as the concept, influencing factors, and implementation measures of fairness in medical AI. Consequently, future research should facilitate interdisciplinary discussions to bridge the cognitive gaps between different fields and enhance the practical implementation of fairness in medical AI.

With the recent launch of the Kunming-Montreal global biodiversity framework (GBF), and the associated monitoring framework, understanding the framework and data needed to support it is crucial. Unfortunately, whilst the monitoring framework was meant to provide key data to monitor progress towards goals and targets, most indicators are too unclear for detection or marking progress. The most common datasets for this task, such as the IUCN redlist of species, have major spatial inaccuracies, and lack the temporal resolution to track progress, whilst point-based datasets lack data from many regions, in addition to species coverage. Utilising existing data will require the careful use of existing data, such as the use of inventories and projecting richness patterns, or filling data gaps before developing species-level models and assessments. As high-resolution data fall outside the scope of explicit indicators within the monitoring framework, using essential biodiversity variables within GEOBON (which are noted in the prelude of the monitoring framework) as a vehicle for data aggregation provides a mechanism for collating the necessary high-resolution data. Ultimately developing effective targets for conservation will require better species data, for which National Biodiversity Strategic Action Plans (NBSAPs) and novel mechanisms for data mobilisation will be necessary. Furthermore, capitalising on climate targets and climate biodiversity synergies within the GBF provides an additional means for developing meaningful targets, trying to develop urgently needed data to monitor biodiversity trends, prioritising meaningful tasks, and tracking our progress towards biodiversity targets.

Despite the massive amount of data and sophisticated computing capacity, Big Tech has evolved into the new data sovereigns that governments must accept in the data era. Data mining and application determine the true value of data; in this regard, Big Tech is tough to replace. The so-called \"Fourth Industrial Revolution\" is reshaping the emerging global order, and at its core are Big Tech firms. They not only express their concerns and spread their values and ideologies but also make their strong presence felt in international affairs, as Big Tech appears to be transforming into a new type of Leviathan. With access to significant amounts of data, the rise of Big Tech poses a challenge to sovereignty\'s exclusivity and superiority, assuming the position of de facto data sovereign. The article holds that the Big Tech firms, by virtue of their technical advantages, have not only deconstructed the traditional concept of sovereignty, but also formed a complex symbiotic relationship.

OBJECTIVE: To determine the risk of pressure injury development in the intensive care unit based on changes in patient conditions.
METHODS: This retrospective study was based on secondary data analysis.
METHODS: Patient data from electronic health records were retrospectively obtained and we included 438 and 1752 patients with and without pressure injury, respectively, among those admitted to the medical and surgical intensive care units (ICUs) from January 2017-February 2020. Changes in patient conditions were analysed based on the first and last objective data values from the day of ICU admission to the day before the onset of pressure injury and categorised as follows: improved, maintained normal, exacerbated and unchanged. Logistic regression was performed to identify the significant predictors of pressure injury development based on 11 variables.
RESULTS: The 11 selected variables were age, body mass index, activity, acute physiology and chronic health evaluation II score, nursing severity level, pulse and albumin, haematocrit, C-reactive protein, total bilirubin and blood urea nitrogen levels. The risk for a pressure injury was high with exacerbation of or persistently abnormal levels of nursing severity, albumin, haematocrit, C-reactive protein, blood urea nitrogen and pulse >100 beat/min.
CONCLUSIONS: Periodic monitoring of haematological variables is important for preventing pressure injury in the intensive care unit.
UNASSIGNED: The study followed STROBE guidelines.
UNASSIGNED: This study contributes to the utilisation of patient data from electronic health records.
CONCLUSIONS: In addition to other pressure injury risk assessment tools, ICU nurses can help prevent pressure injuries by assessing patients\' blood test results, thereby promoting patient safety and enhancing the efficacy of nursing practice.

BACKGROUND: In this paper, a semiautomatic image segmentation method for the serialized body slices of the Visible Human Project (VHP) is proposed.
METHODS: In our method, we first verified the effectiveness of the shared matting method for the VHP slices and utilized it to segment a single image. Then, to meet the need for the automatic segmentation of serialized slice images, a method based on the parallel refinement method and flood-fill method was designed. The ROI (region of interest) image of the next slice can be extracted by using the skeleton image of the ROI in the current slice.
RESULTS: Utilizing this strategy, the color slice images of the Visible Human body can be continuously and serially segmented. This method is not complex but is rapid and automatic with less manual participation.
CONCLUSIONS: The experimental results show that the primary organs of the Visible Human body can be accurately extracted.

Sexually transmitted diseases (STDs) are a serious issue worldwide. With the popularity of the internet, online health information-seeking behavior (OHISB) has been widely adopted to improve health and prevent disease.
This study aimed to investigate the short-term and long-term effects of different types of OHISBs on STDs, including syphilis, gonorrhea, and AIDS due to HIV, based on the Baidu index.
Multisource big data were collected, including case numbers of STDs, search queries based on the Baidu index, provincial total population, male-female ratio, the proportion of the population older than 65 years, gross regional domestic product (GRDP), and health institution number data in 2011-2018 in mainland China. We categorized OHISBs into 4 types: concept, symptoms, treatment, and prevention. Before and after controlling for socioeconomic and medical conditions, we applied multiple linear regression to analyze associations between the Baidu search index (BSI) and Baidu search rate (BSR) and STD case numbers. In addition, we compared the effects of 4 types of OHISBs and performed time lag cross-correlation analyses to investigate the long-term effect of OHISB.
The distributions of both STD case numbers and OHISBs presented variability. For case number, syphilis, and gonorrhea, cases were mainly distributed in southeastern and northwestern areas of China, while HIV/AIDS cases were mostly distributed in southwestern areas. For the search query, the eastern region had the highest BSI and BSR, while the western region had the lowest ones. For 4 types of OHISB for 3 diseases, the BSI was positively related to the case number, while the BSR was significantly negatively related to the case number (P<.05). Different categories of OHISB have different effects on STD case numbers. Searches for prevention tended to have a larger impact, while searches for treatment tended to have a smaller impact. Besides, due to the time lag effect, those impacts would increase over time.
Our study validated the significant associations between 4 types of OHISBs and STD case numbers, and the impact of OHISBs on STDs became stronger over time. It may provide insights into how to use internet big data to better achieve disease surveillance and prevention goals.

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