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Abstract Nonalcoholic fatty liver disease (NAFLD) is a serious threat to public health, but its underlying mechanism remains poorly understood. In screening important genes using Gene Importance Calculator (GIC) we developed previously, ribosomal modification protein rimK-like family member A (RIMKLA) was predicted as one essential gene but its functions remained largely unknown. The current study determined the roles of RIMKLA in regulating glucose and lipid metabolism. RIMKLA expression was reduced in livers of human and mouse with NAFLD. Hepatic RIMKLA overexpression ameliorated steatosis and hyperglycemia in obese mice. Hepatocyte-specific RIMKLA knockout aggravated high-fat diet (HFD)-induced dysregulated glucose/lipid metabolism in mice. Mechanistically, RIMKLA is a new protein kinase that phosphorylates betaine-homocysteine S-methyltransferase 1 (BHMT1) at threonine 45 (Thr45) site. Upon phosphorylation at Thr45 and activation, BHMT1 eliminated homocysteine (Hcy) to inhibit the activity of transcription factor activator protein 1 (AP1) and its induction on fatty acid synthase (FASn) and cluster of differentiation 36 (CD36) gene transcriptions, concurrently repressing lipid synthesis and uptake in hepatocytes. Thr45 to alanine (T45A) mutation inactivated BHMT1 to abolish RIMKLA’s repression on Hcy level, AP1 activity, FASn/CD36 expressions, and lipid deposition. BHMT1 overexpression rescued the dysregulated lipid metabolism in RIMKLA-deficient hepatocytes. In summary, RIMKLA is a novel protein kinase that phosphorylates BHMT1 at Thr45 to repress lipid synthesis and uptake. Under obese condition, inhibition of RIMKLA impairs BHMT1 activity to promote hepatic lipid deposition.

Abstract With childhood hypertension emerging as a global public health concern, understanding its associated factors is crucial. This study investigated the prevalence and associated factors of hypertension among Chinese children. This cross-sectional investigation was conducted in Pinghu, Zhejiang province, involving 2,373 children aged 8–14 years from 12 schools. Anthropometric measurements were taken by trained staff. Blood pressure (BP) was measured in three separate occasions, with an interval of at least two weeks. Childhood hypertension was defined as systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) ≥ age-, sex-, and height-specific 95th percentile, across all three visits. A self-administered questionnaire was utilized to collect demographic, socioeconomic, health behavioral, and parental information at the first visit of BP measurement. Random forest (RF) and multivariable logistic regression model were used collectively to identify associated factors. Additionally, population attributable fractions (PAFs) were calculated. The prevalence of childhood hypertension was 5.0% (95% confidence interval [CI]: 4.1–5.9%). Children with body mass index (BMI) ≥ 85th percentile were grouped into abnormal weight, and those with waist circumference (WC) > 90th percentile were sorted into central obesity. Normal weight with central obesity (NWCO, adjusted odds ratio [aOR] = 5.04, 95% CI: 1.96–12.98), abnormal weight with no central obesity (AWNCO, aOR = 4.60, 95% CI: 2.57–8.21), and abnormal weight with central obesity (AWCO, aOR = 9.94, 95% CI: 6.06–16.32) were associated with an increased risk of childhood hypertension. Childhood hypertension was attributable to AWCO mostly (PAF: 0.64, 95% CI: 0.50–0.75), followed by AWNCO (PAF: 0.34, 95% CI: 0.19–0.51), and NWCO (PAF: 0.13, 95% CI: 0.03–0.30). Our results indicated that obesity phenotype is associated with childhood hypertension, and the role of weight management could serve as potential target for intervention.

The objective of the study was to investigate the effect of radio frequency parameters on the thawing time and the uniformity. During the radio frequency thawing of frozen chicken breast at 27.12 MHz and 6 kW, the effects of operating parameters such as electrode gap (8, 9, 10, and 11 cm), voltage (70%, 75%, and 80%) and sample thickness (4, 5, and 6 cm) on the pattern of temperature rise and the uniformity were examined. The results showed that with decreasing electrode gap and increasing voltage and sample thickness, the temperature rise accelerated and the uniformity index decreased. The optimum parameters were determined as 9 cm and 75% for electrode gap and voltage, respectively based on the thawing time and temperature distribution. Compared with thawing at ambient temperature (20 ℃, 6 h), the thawing time with radio frequency was shortened to 54.67 min, and the total viable count, the drip loss and total volatile basic nitrogen (T-VBN) content were significantly reduced (P < 0.05). The results of this study provide a theoretical basis and technical support for the practical application of radio frequency technology in the field of meat thawing.

Background: Medical progress has often been hindered by the inherent limitations of human ability to process large volumes of data. The application of Artificial Intelligence (AI) can help overcome this constraint, particularly in the field of trauma. Purpose and Objectives: This study aims to analyze the application of artificial intelligence in the field of trauma through visualization tools, predict future research hotspots, and explore the potential applications of related technologies in the field of trauma, especially traumatic brain injury (TBI). Materials and Methods: Based on the Web of Science database, this study utilized visualization tools such as CiteSpace, VOSviewer, and SciMAT to create a knowledge map of AI applications in trauma from 1979 to 2022. Results: The analysis indicates that traumatic brain injury (TBI) will be a focal point for future research on the use of AI in trauma. Additionally, terms related to machine learning, including Artificial Neural Network and Convolutional Neural Network, are expected to be extensively employed in trauma detection and prediction. These targeted algorithms hold significant potential for groundbreaking applications in TBI. Conclusion: Artificial intelligence, especially machine learning techniques, will play a crucial role in the research and application of trauma, particularly TBI. In the future, these technologies are expected to provide new methods and perspectives for TBI detection, prediction, and treatment.

Purpose: Capillary Refill Time (CRT) measurement has gained increasing attention in the field of sepsis and septic shock. Recognizing pressure as a fundamental determinant in CRT measurement is crucial for establishing a standardized CRT measurement procedure. In this preliminary study, we elucidated the optimal pressing strength for CRT measurement by analyzing the CRTs measured under varying pressures. Method: Seventeen healthy individuals were enlisted to undergo CRT tests on their fingertips at various pressure levels. The applied force was initiated at 0.5N and incrementally increased by 0.5N until it reached 10.5N. An integrated Photoplethysmography (PPG) device was employed to capture fluctuations in light intensity. The CRT was automatically derived from the PPG signals via a specialized algorithm. The study included correlation assessment and reliability evaluation. Box plot and Bland-Altman plot were used to visualize the impact of pressure levels on CRTs. Results: A dataset of 1414 CRTs across 21 pressures showed significant differences (Kruskal-Wallis test, p

Existing deep learning-based hyperspectral anomaly detection methods typically perform anomaly detection by reconstructing a clean background. However, for the deep networks, there are many parameters that need to be adjusted. To reduce parameters of network and improve the performance of anomaly detection, a light CNN based on residual learning and background estimation was proposed. Different from traditional methods, the proposed method could directly learn anomaly features rather than background features. First, during the training stage, a background estimation method based on non-central convolution kernels was used to obtain the pseudo-background. Second, to purify the pseudo-background, a pair down-sampling method and a joint loss that combines cross-approximation background loss and consistency loss were proposed. Third, the anomaly matrix was obtained by the difference between the hyperspectral image (HSI) and the pseudo-background. Fourth, a light CNN with three layers was proposed to extract features of the anomaly matrix. Finally, during the prediction stage, anomaly detection results were calculated from the predicted anomaly matrix obtained by light CNN through the Mahalanobis distance. Experiments were conducted with multiple metrics on five real-world datasets. Compared with eight state-of-the-art methods, the proposed method achieved the superior performance in both qualitative and quantitative evaluations.