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To reveal the influence mechanism of the grinding surface quality of 20CrMnTi steel components on the tribological characteristics and contact fatigue performance, accelerated tests for sliding friction wear and fatigue damage were carried out. Tribological characteristics and contact fatigue performance get worse with increasing surface roughness while getting better with increasing surface microhardness. Residual compressive stress is conducive to inhibiting the initiation and propagation of cracks and promoting contact fatigue performance. Additionally, mechanical friction, abrasive wear, adhesive wear and fatigue damage coexist and form a competing failure mechanism under the synergistic effect of frictional wear and contact fatigue failure. The damage process mainly manifests as wear, stress concentration induced fatigue, microcracks, pitting, and spalling in the shallow layer. This study is more beneficial to promote the 20CrMnTi steel transmission parts manufacturing products for high precision, low damage, and long life.

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.

Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological research. The experimental validation of drug–ADR associations often entails expensive and time-consuming investigations. Thus, a computational model to predict ADRs from known associations is essential for enhanced efficiency and cost-effectiveness. Here, we propose BiMPADR, a novel model that integrates drug gene expression into adverse reaction features using a message passing neural network on a bipartite graph of drugs and adverse reactions, leveraging publicly available data. By combining the computed adverse reaction features with the structural fingerprints of drugs, we predict the association between drugs and adverse reactions. Our models obtained high AUC (area under the receiver operating characteristic curve) values ranging from 0.861 to 0.907 in an external drug validation dataset under differential experiment conditions. The case study on multiple BET inhibitors also demonstrated the high accuracy of our predictions, and our model’s exploration of potential adverse reactions for HWD-870 has contributed to its research and development for market approval. In summary, our method would provide a promising tool for ADR prediction and drug safety assessment in drug discovery and development.

Background This article examines the association between population density, maternal mortality, and under-5 mortality in countries throughout the world, as well as the mediating impacts of the Universal Health Coverage Service Coverage Index (UHC-SCI). Methods The World Health Organization’s website provided data on maternal mortality and the Universal Health Coverage Service Coverage Index for the years 2000–2020. The World Bank database included information on population density and under-5 mortality rates for nations between 2000 and 2020. Panel regressions were used to examine the association between population density and maternal and under-5 mortality in each nation, as well as the mediating influence of the Universal Health Coverage Service Coverage Index, while accounting for economic, environmental, and medical factors. Finally, data is divided into regressions based on World Bank member countries’ income levels to examine heterogeneity. Results The study included 175 countries and found a significant negative correlation between population density, maternal mortality, and under-5 mortality ( B = -1.015, -1.146, P  < 0.05). The Universal Health Coverage Service Coverage Index mediated this relationship ( B = -1.044, -1.141, P  < 0.05). Conclusions Increasing population density in countries around the world has helped to reduce maternal and child mortality. As population density has increased, so has the level of the Universal Health Coverage Service Coverage Index, which has proven effective in lowering maternal and under-5 mortality. Governments should plan interventions to build basic health facilities and allocate resources to health services based on population density, level of economic development, and the current state of their health systems, with the goal of stabilizing the rate of change in maternal and under-5 mortality and, eventually, achieving the Sustainable Development Goals.

Objectives We aimed to evaluate the global burden and trends of lower respiratory infections (LRIs) attributable to non-optimal temperatures between 1990 and 2021, focusing on age, period, and cohort effects as well as health inequalities to inform targeted public health policies. Methods Using the Global Burden of Disease 2021 database, we obtained the age-standardized mortality rate (ASMR) and disability-adjusted life-years rate (ASDR) for LRIs related to non-optimal temperatures. We calculated estimated annual percentage changes (EAPC) to assess LRIs burden trends and applied age-period-cohort modeling to quantify age, period, and cohort effects. Health inequalities were evaluated using the slope index of inequality and the concentration index. Results In 2021, the highest ASDR for LRIs due to high temperatures occurred in children under 5 (347.66/100,000), whereas the highest ASMR for LRIs due to low temperatures occurred in adults aged ≥ 65 (338.49/100,000). Globally, the LRIs burden from non-optimal temperatures declined (EAPC: ASMR –2.48; ASDR –3.33). However, among the five climate zones, the LRIs burden in the boreal zone due to high temperatures increased (EAPC: ASMR 24.14; ASDR 45.14), whereas all other climate zones showed decreasing trends. In lower Sociodemographic Index (SDI) regions, the high-temperature–related LRIs burden was more pronounced. Relative inequities driven by non-optimal temperatures worsened in low-SDI regions. Conclusion From 1990 to 2021, the global burden of LRIs attributable to non-optimal temperatures declined overall; however, high-temperature–related LRIs increased in boreal zones. These health inequalities underscore the urgent need for targeted climate adaptation policies, such as providing international assistance, improving infrastructure, offering healthcare resources, and promoting vaccine coverage, particularly for vulnerable populations in low-SDI regions and boreal zones.

With the realization of global navigation satellite system (GNSS) completion, GNSS reflectometry (GNSS-R) has become increasingly popular due to the advantages of global coverage and the availability of multiple sources in terms of earth remote sensing. This paper analyzes the Beidou navigation satellite system (BDS) signal reflection detection of multiple satellites and multiple moving targets under multiple-input and multiple-output (MIMO) radar systems and proposes a series of methods to suppress multiple Doppler phase influences and improve the range detection property. The simulation results show the restored target peaks, which match the RCS data more accurately, with the GNSS-R Doppler phase influence removed, which proves the proposed method can improve target recognition and detection resolution performance.

Efficiently harnessing the urban cool island effect associated with large urban aquatic bodies holds significant importance in mitigating the urban heat island (UHI) effect and enhancing the quality of residential living. This study focuses on Tianjin’s Meijiang Lake and its surrounding 47 residential areas, combining Landsat 8 remote sensing satellite data with geographic information system (GIS) buffer analyses and multiple linear regression analyses to reveal the summer thermal characteristics of residential waterfront areas with diverse spatial layouts. The results indicate that: (1) Meijiang Lake’s effective cooling radius extends up to 130 m from the water’s edge, achieving a maximum temperature reduction of 14.44%. Beyond 810 m, the cooling effect diminishes significantly. (2) Waterfront distance (WD), building density (BD), building width (L) and normalized difference vegetation index (NDVI) emerge as the primary factors influencing changes in average land surface temperature (ΔLST) in residential areas. The degrees of influence are ordered as follows: BD > WD > NDVI > L. “Dispersed” pattern residential areas exhibit the most favorable thermal environments, which are primarily influenced by WD, while “parallel” pattern residential areas demonstrate the least favorable conditions, primarily due to WD and NDVI. (3) The direct adjacency of residential areas to large-scale aquatic bodies proves to be the most effective approach for temperature reduction, resulting in a 5.03% lower average temperature compared to non-adjacent areas. Consequently, this study derives strategies for improving the thermal environment via the regulation of spatial planning elements in residential areas, including waterfront patterns, vegetation coverage, WD, and BD.

Background: There have been significant advancements in melanoma therapies. BET inhibitors (BETis) show promise in impairing melanoma growth. However, identifying BETi-sensitive melanoma subtypes is challenging. Methods and Results: We analyzed 48 melanoma cell lines and 104 patients and identified two acetylation-immune subtypes (ALISs) in the cell lines and three ALISs in the patients. ALIS I, with high HAT1 and low KAT2A expression, showed a higher sensitivity to the BETi JQ-1 than ALIS II. ALIS III had low HAT1 expression. The TAD2B expression was low in ALIS I and II. KAT2A and HAT1 expressions were negatively correlated with the methylation levels of their CG sites (p = 0.0004 and 0.0003). Immunological gene sets, including B cell metagenes, activated stroma-related genes, fibroblast TGF response signatures (TBRS), and T cell TBRS-related genes, were up-regulated in ALIS I. Furthermore, KAT2A played a key role in regulating BETi sensitivity. Conclusions: The sensitivity of ALIS I to the BETi JQ-1 may be due to the inhibition of BETi resistance pathways and genes by low KAT2A expression and the dysregulation of the immune microenvironment by high HAT1 expression resulting from the absence of immune cells. ALIS I had the worst progression but showed sensitivity to BETi and B-cell-related immunotherapy, despite not responding to BRAF inhibitors.

Background As older people have complex medical needs and still encounter challenges in accessing online health information, the relationship between Internet use and the choice of medical institution made by them is unclear, and we aimed to examine this relationship. Methods Data from the newly released 2020 China Family Panel Survey database were used. Furthermore, we used descriptive statistics to analyze the background characteristics of the sample and a logistic regression model to estimate the impact of Internet use on the choice of medical institution made by older adults. We conducted a stratified analysis to explore the influence of different characteristics on the relationship between Internet use and the choice of medical institution. Results Totally 4,948 older adults were included. Multivariate logistic regression showed that, compared to non-Internet users, Internet users were less likely to choose community health service centers over general hospitals ( P  < 0.001, OR = 0.667, 95CI%: 0.558–0.797). The subgroup analyses found that Internet use only had an impact on the choice of medical institution in older adults aged 65–69 years, those with partners, those with primary or secondary education, those residing in urban areas, those without medical insurance, those with a self-rated health status as average or healthy, those with unchanged or better health trend, and those without chronic disease. The effect of Internet use on the choice of medical institution did not differ by sex, satisfaction, or trust in doctors. Conclusion Internet use may significantly affect older adults’ tendency to choose general hospitals to meet their daily medical needs. The subgroup analyses indicated that different characteristics of older people affected this association.

Accurate detection of abnormal cervical cells in cervical cancer screening increases the chances of timely treatment. The vigorous development of deep learning methods has established a new ecosystem for cervical cancer screening, which has been proven to effectively improve efficiency and accuracy of cell detection in many studies. Although many contributing studies have been conducted, limited public datasets and time-consuming collection efforts may hinder the generalization performance of those advanced models and restrict further research. Through this work, we seek to provide a large dataset of cervical cytology images with exhaustive annotations of abnormal cervical cells. The dataset consists of 8,037 images derived from 129 scanned Thinprep cytologic test (TCT) slide images. Furthermore, we performed evaluation experiments to demonstrate the performance of representative models trained on our dataset in abnormal cells detection.