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This paper presents a high-performance general-purpose no-reference (NR) image quality assessment (IQA) method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between different color channels and the two-dimensional entropy are calculated. In the frequency domain, the statistical characteristics of the two-dimensional entropy and the mutual information of the filtered subband images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier (SVC) for distortion classification and support vector regression (SVR) are utilized for the quality prediction, to obtain the final quality assessment score. The proposed method, which we call entropy-based no-reference image quality assessment (ENIQA), can assess the quality of different categories of distorted images, and has a low complexity. The proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed a superior performance. The experimental results confirmed that the proposed ENIQA method has a high consistency of objective and subjective assessment on color images, which indicates the good overall performance and generalization ability of ENIQA. The implementation is available on github https://github.com/jacob6/ENIQA.

Background The dose perturbation effect of immobilization devices is often overlooked in intensity-modulated radiation therapy (IMRT) for breast cancer (BC). This retrospective study assessed the dosimetric effects of supine immobilization devices on the skin using a commercial treatment planning system. Methods Forty women with BC were divided into four groups according to the type of primary surgery: groups A and B included patients with left and right BC, respectively, who received 50 Gy radiotherapy in 25 fractions after radical mastectomy, while groups C and D included patients with left and right BC, respectively, who received breast-conservation surgery (BCS) and 40.05 Gy in 15 fractions as well as a tumor bed simultaneous integrated boost to 45 Gy. A 0.2-cm thick skin contour and two sets of body contours were outlined for each patient. Dose calculations were conducted for the two sets of contours using the same plan. The dose differences were assessed by comparing the dose-volume histogram parameter results and by plan subtraction. Results The supine immobilization devices for BC resulted in significantly increased skin doses, which may ultimately lead to skin toxicity. The mean dose increased by approximately 0.5 and 0.45 Gy in groups A and B after radical mastectomy and by 2.7 and 3.25 Gy in groups C and D after BCS; in groups A–D, the percentages of total normal skin volume receiving equal to or greater than 5 Gy (V 5 ) increased by 0.54, 1.15, 2.67, and 1.94%, respectively, while the V 10 increased by 1.27, 1.83, 1.36, and 2.88%; the V 20 by 0.85, 1.87, 2.76, and 4.86%; the V 30 by 1.3, 1.24, 10.58, and 11.91%; and the V 40 by 1.29, 0.65, 10, and 10.51%. The dose encompassing the planning target volume and other organs at risk, showed little distinction between IMRT plans without and with consideration of immobilization devices. Conclusions The supine immobilization devices significantly increased the dose to the skin, especially for patients with BCS. Thus, immobilization devices should be included in the external contour to account for dose attenuation and skin dose increment. Trial registration This study does not report on interventions in human health care.

Background The emergence of multidrug-resistant bacteria poses a severe challenge to infection treatment, creating an urgent need for novel antimicrobial agents. Monomers derived from natural products exhibit multi-mechanism antibacterial properties with low resistance development potential. However, evaluation of these monomers is frequently hampered by color interference in traditional turbidimetric methods. This study developed a high-throughput fully automated bacterial growth curve monitor (HTFA-BGM) to provide a new tool for screening and assessing antimicrobial monomers. Methods The HTFA-BGM was designed and developed based on the principle of scattering nephelometry. Its performance in determining the minimum inhibitory concentration (MIC) was compared with the microdilution and the tube dilution methods. After establishing the instrument's MIC determination criterion, it was used to evaluate and screen the antibacterial effects of Traditional Chinese Medicine (TCM) monomers. Results The HTFA-BGM could screen MICs of 40 monomers against one strain or one monomer against 40 strains simultaneously, unaffected by compound color. When a threshold of ≤ 10% for the ratio of sample turbidity change to bacterial suspension turbidity change was used as the MIC criterion, there was no significant difference between the HTFA-BGM method and the microdilution or tube dilution methods. The HTFA-BGM was used to evaluate 15 monomers (including polyphenolic, quinonoid, and other types of compounds), and the MIC of rhein against 90 strains of Methicillin-Resistant Staphylococcus aureus (MRSA) was 8–64 μg/mL. The synergistic rates of rhein combined with penicillin, cefoxitin, cefazolin or oxacillin against MRSA in vitro were 30% (27/90), 63% (57/90), 57% (51/90), and 58% (52/90), respectively, whereas the additive rates were 70% (63/90), 37% (33/90), 43% (39/90), and 42% (38/90), respectively. The MIC of oleanolic acid against 13 isolates of vancomycin-resistant Enterococcus (VRE) was 16–32 μg/mL. When combined with vancomycin, vancomycin MIC decreased from 256–1024 to 1 μg/mL. Conclusion The HTFA-BGM enables automatic, real-time, and dynamic monitoring of bacterial growth curves. Compared with the microdilution method, which determines the MIC on the basis of endpoint turbidity, this instrument not only has improved efficiency but also yields more objective and accurate results. This method specifically resolves the interference caused by the intrinsic color of colored TCM monomers such as rhein during their determination tests. Therefore, it is better suited for evaluating and screening antibacterial TCM monomers. The HTFA-BGM demonstrates excellent practical applicability and warrants broad promotion and application.

Electrical acupoint stimulation (EAS) is proposed as a potentially beneficial treatment for patients suffering from knee osteoarthritis (KOA). This systematic review and meta-analysis aims to assess the assess the effectiveness and safety of EAS for KOA. To identify eligible RCTs, a systematic search for eligible RCTs is conducted through 6 November 2024 in 12 electronic literature databases, utilizing relevant keywords. The analysis applies a random-effects model to compute RRs and 95% CIs for the dichotomous outcome, alongside the SMD and 95% CIs for continuous outcomes. This meta-analysis includes 63 RCTs with a total of 6475 participants. The pooled analysis reveals that EAS increases the overall response rate by 19.5% (RR: 1.195, 95% CI: 1.130 to 1.264, P < 0.001; low certainty). For continuous outcomes, EAS produces large effects on WOMAC total score (SMD = −1.72, 95% CI −2.19 to −1.24; P < 0.001; low certainty), VAS score (SMD = −1.92, 95% CI −2.44 to −1.39; P < 0.001; very low certainty), WOMAC stiffness (SMD = −1.12, 95% CI −1.65 to −0.59; P < 0.001; low certainty) and Lysholm score (SMD = 1.10, 95% CI 0.47 to 1.73; P = 0.001; low certainty). It yields medium effects on WOMAC pain (SMD = −0.74, 95% CI −1.10 to −0.37; P < 0.001; low certainty), Lequesne index (SMD = −0.70, 95% CI −0.97 to −0.42; P < 0.001; low certainty) and WOMAC function (SMD = −0.51, 95% CI −0.97 to −0.05; P = 0.03; low certainty). There are no significant effects on peak quadriceps torque (SMD = 0.21, 95% CI −0.42 to 0.83; P = 0.518; low certainty) or knee range of motion (SMD = 0.66, 95% CI −0.07 to 1.38; P = 0.077; low certainty). Trial sequential analysis indicates that the required information size is met. This review suggests that EAS may be an effective and relatively safe adjunctive option for KOA; however, the certainty of the evidence is low due to substantial heterogeneity and potential biases. Higher-quality, rigorously designed RCTs with standardized reporting are needed to confirm these findings.

In recent years, deep learning (DL) networks have been widely used in super-resolution (SR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided single image super-resolution (SISR) method is proposed in DL architecture, in order to achieve a nice tradeoff between perceptual quality and distortion measure of the SR result. Unlike existing DL-based SR algorithms, an IQA net is introduced to extract perception features from SR results, calculate corresponding loss fused with original absolute pixel loss, and guide the adjustment of SR net parameters. To solve the problem of heterogeneous datasets used by IQA and SR networks, an interactive training model is established via cascaded network. We also propose a pairwise ranking hinge loss method to overcome the shortcomings of insufficient samples during training process. The performance comparison between our proposed method with recent SISR methods shows that the former achieves a better tradeoff between perceptual quality and distortion measure than the latter. Extensive benchmark experiments and analyses also prove that our method provides a promising and opening architecture for SISR, which is not confined to a specific network model.

The rising prevalence of cardiometabolic multimorbidity (CMM), characterized by the coexistence of two or more cardiometabolic disorders, poses a significant public health challenge in aging populations. While ambient air pollution is a recognized environmental risk factor, its long-term impact on CMM remains underexplored, particularly in China. Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS, 2015 wave), we analyzed 9,830 participants aged ≥ 45 years. CMM was defined as the concurrent presence of two or more conditions: diabetes/hyperglycemia, cardiovascular diseases (myocardial infarction, angina pectoris, coronary artery disease, or heart failure, or stroke. Annual pollutant exposures were estimated using a machine learning-based spatiotemporal model (space-time extremely randomized trees) based on residential addresses. Generalized linear models adjusted for sociodemographic, lifestyle, and meteorological covariates were employed to assess odds ratios (ORs) per interquartile range (IQR) increase in pollutants. Chronic exposure to particulate matter (PM) 10 demonstrated a consistent positive association with CMM prevalence across models. In fully adjusted analyses, each IQR increase in PM10 was associated with elevated CMM risk (OR = 1.01, 95% CI: 1.00–1.02, P  = 0.039). Sensitivity analyses, including alternative exposure windows and adjustments for regional variations, reinforced PM10’s robust association with CMM. Other pollutants (PM2.5, and sulfur dioxide) showed weaker or inconsistent associations. Long-term exposure to ambient particulate matter, particularly PM10, is significantly linked to increased CMM prevalence in China’s aging population. The findings of this study provide epidemiological evidence, laying the foundation for future cohort studies and mechanistic investigations.

Effective path planning in complex underwater environments serves as a critical determinant of autonomous underwater vehicle (AUVs) energy efficiency, while simultaneously influencing sensor operational demands and battery state-of-charge (SOC) dynamics. Systematic trajectory tracking emerges as a pivotal methodology for SOC optimization, enabling enhanced energy management through precision navigation control. This paper proposes a path planning and trajectory tracking control framework for autonomous underwater vehicles (AUVs) combined with battery state of charge (SOC) optimization. The framework incorporates the Grasshopper Optimization Algorithm (GOA) with the Artificial Potential Field Algorithm (APF) to achieve global path planning and local path optimization while minimizing energy consumption as an objective. Specifically, GOA is used for global path planning. APF further optimizes the path by introducing a SOC optimization strategy, in which high SOC consumption points are regarded as repulsive points and low SOC consumption points are regarded as attractive points. In addition, the trajectory tracking control adopts the model predictive control (MPC) method to ensure the accurate tracking of the planned path and dynamically manage the SOC states. Simulation results show that the proposed framework outperforms traditional methods in obstacle avoidance capability and SOC consumption, effectively improving energy efficiency and trajectory tracking accuracy.

Recently, miRNAs have become a promising biomarker for disease diagnostics. miRNA-145 is closely related to strokes. The accuracy determination of miRNA-145 (miR-145) in stroke patients still remains challenging due to its heterogeneity and low abundance, as well as the complexity of the blood matrix. In this work, we developed a novel electrochemical miRNA-145 biosensor via subtly coupling the cascade strand displacement reaction (CSDR), exonuclease III (Exo III), and magnetic nanoparticles (MNPs). The developed electrochemical biosensor can quantitatively detect miRNA-145 ranging from 1 × 102 to 1 × 106 aM with a detection limit as low down as 100 aM. This biosensor also exhibits excellent specificity to distinguish similar miRNA sequences even with single-base differences. It has been successfully applied to distinguish healthy people from stroke patients. The results of this biosensor are consistent with the results of the reverse transcription quantitative polymerase chain reaction (RT-qPCR). The proposed electrochemical biosensor has great potential applications for biomedical research on and clinical diagnosis of strokes.

Domestic burning emits large amounts of pollutants into the ambient air due to incomplete and inefficient combustion, with significant impacts on indoor air quality and human health. Northeast China is one of the major contributors to domestic burning emissions in China; however, the high-resolution emissions inventories of domestic biomass and coal burning in Northeast China are lacked, which are needed to estimate the extent of its impact. In this study, we established a town-level emissions inventory of gaseous pollutants and particulate matter (PM) from domestic biomass and coal burning, based on per household consumption in each town in rural region of Northeast China. The results revealed that biomass burning was the major domestic burning source over the region in 2016. Domestic biomass burning is the major contributor to PM and volatile organic compounds (VOCs) emissions, while coal burning is the major contributor to SOP 2 emissions. High emissions intensities were concentrated around the cities of Harbin, Suihua, Changchun, Qiqihar, and Chifeng, each with emissions intensity for PM2.5 and VOCs of more than 2000 Mg per 27 km × 27 km grid cell. Additionally, there are three burning peaks (6–7 am, 12 pm and 4–7 pm) during both the heating (from October to April) and non-heating seasons (from May to September), due to cooking and heating. The burning peaks in the non-heating season were more notable than those in the heating season. These results suggest that the government should pay more attention to domestic biomass and coal burning in rural areas, in order to reduce pollutant emissions and control regional haze during the heating season.

Objectives This study used resting state functional magnetic resonance imaging (rs‐fMRI) technology to explore the characteristics of brain functional activity in migraine patients without aura (MwoA) in middle and high altitude areas during interictal periods through two analysis methods, the regional homogeneity (ReHo) and amplitude of low‐frequency fluctuation (ALFF). Methods This study was a prospective research that included 41 patients with MwoA in the interictal phase, who visited the Department of Neurology at Qinghai Provincial People's Hospital between January 2023 and January 2024. 39 healthy controls (HCs) matched for age and sex were also recruited. Results Compared with HCs group, the ALFF values of right superior temporal gyrus and the right hippocampus in MwoA group at mid‐to‐high altitude were decreased (voxel level p < 0.001, cluster level p < 0.05, Gaussian random field, GRF corrected). The ReHo values of bilateral rectus gyrus and left cerebellum in MwoA group at mid‐to‐high altitude were significantly increased, while the ReHo values of left cingulate gyrus, bilateral precuneus and bilateral supplementary motor area were significantly decreased (voxel level p < 0.001, cluster level p < 0.05, GRF corrected). The correlation analysis showed that the duration of disease in MwoA group was negatively correlated with the z‐ALFF value of the right hippocampus(r = −0.56, p = 0.004, Bonferroni correction). The HIT‐6 score was negatively correlated with the z‐ALFF value of the right superior temporal gyrus (r = −0.48, p = 0.001, Bonferroni correction). The SDS score was negatively correlated with the bilateral precuneus z‐ReHo values (r = −0.42, p = 0.03, L; r = −0.46, p = 0.01, R, Bonferroni correction). Conclusion Several brain regions in MwoA patients from mid‐to‐high altitude areas exhibit abnormal spontaneous neural activity through ALFF and ReHo assessments. These brain regions are closely associated with pain processing, cognitive functions, motor control, attention, and emotional regulation. The functional abnormalities in these regions may be relevant to the pathophysiology of MwoA. A Resting‐state fMRI reveals altered functional connectivity within pain‐processing and default mode networks in migraine without aura patients residing at middle‐to‐high altitudes, which correlates with headache severity.