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Abstract Photon‐counting detector‐based computed tomography (PCD‐CT) is an advanced realization of spectral CT, the multi‐energy projection data is captured from the same object, hence, CT images can provide additional spectral resolution, making it possible to perform material decomposition. However, multiple projections may have a low signal‐to‐noise ratio (SNR), such that CT images suffer from noise. To handle this problem, a spectral CT image reconstruction method based on anisotropic sparse transformation (AST) is proposed. To increase the quality of reconstruction, AST through an anisotropic guided filter (AGF) and L0 quasi norm is proposed. Then, as a new regularization, AST is introduced into an iterative reconstruction process, generating an AST‐based method. Moreover, to utilize the correlation among projection data, the average image serves as the guidance image of AGF, it varies with the iterative index, resulting in a technique of dynamic average image (DAI). The AST‐based model involves L0 quasi norm minimization, hence an effective strategy is employed to solve the corresponding problem. A series of experiments were performed. The experiment showed that, compared to other listed methods, the result of the AST‐based method can achieve better visualization and higher quantitative indexes, hence it has application potential in the medical imaging field.

Abstract Assessing street walkability is a critical agenda in urban planning and multidisciplinary research, as it facilitates public health, community cohesion, and urban sustainability. Existing evaluation systems primarily focus on objective measurements, often neglecting subjective assessments and the diverse walking needs influenced by different urban spatial elements. This study addresses these gaps by constructing a comprehensive evaluation framework that integrates both subjective and objective dimensions, combining three neighbourhood indicators: Macro-Scale Index, Micro-Scale Index, and Street Walking Preferences Index. A normalization weighting method synthesizes these indicators into a comprehensive index. We applied this framework to assess the street environment within Beijing’s Fifth Ring Road. The empirical results demonstrate that: (1) The framework reliably reflects the distribution of walkability. (2) The three indicators show both similarities and differences, underscoring the need to consider the distinct roles of community and street-level elements and the interaction between subjective and objective dimensions. (3) In high-density cities with ring-road development patterns, the Macro-Scale Index closely aligns with the Comprehensive Index, demonstrating its accuracy in reflecting walkability. The proposed framework and findings offer new insights for street walkability research and theoretical support for developing more inclusive, sustainable and walkable cities.

Abstract Accurate molecular representation of compounds is a fundamental challenge for prediction of drug targets and molecular properties. In this study, we present a molecular video-based foundation model, named VideoMol, pretrained on 120 million frames of 2 million unlabeled drug-like and bioactive molecules. VideoMol renders each molecule as a video with 60-frame and designs three self-supervised learning strategies on molecular videos to capture molecular representation. We show high performance of VideoMol in predicting molecular targets and properties across 43 drug discovery benchmark datasets. VideoMol achieves high accuracy in identifying antiviral molecules against common diverse disease-specific drug targets (i.e., BACE1 and EP4). Drugs screened by VideoMol show better binding affinity than molecular docking, revealing the effectiveness in understanding the three-dimensional structure of molecules. We further illustrate interpretability of VideoMol using key chemical substructures.

Objective To evaluate clinical efficacy of chronic disease management based on a case management model in patients with allergic bronchial asthma. Methods Patients with allergic bronchial asthma admitted to the outpatient of Department of Pulmonary and Critical Care Medicine of Beijing Tsinghua Changgung Hospital from February 2020 to February 2023 were retrospectively included. The effect of case management on asthma symptom control and lung function was analyzed by tracking the asthma symptom control （ACT） score and lung function at 1， 3， 6 and 12 months after case management. Results A total of 175 patients with allergic asthma were included， comprising 70 male （40%） and 105 female （60%）， with a mean age of （45.58±13.78） years. Among these patients， 139 （79.4%） cases were complicated with allergic rhinitis. Pulmonary function parameters， including FEV1%pred， FEV1， FEV1/FVC， MMEF75/25%pred and PEF， were improved significantly from （87.09±18.56）%， （2.65±0.88） L， （75.09±11.93）%， 52.20% （34.70%， 69.30%）， and （6.65±1.94） L/s to （97.80±13.81）%， （2.96±0.76） L， （84.72±11.18）%， 69.20% （53.70%， 86.90%）， and （7.57±1.89） L/s （all P < 0.001）， respectively. ACT score showed a 100% good control rate after 1-year case management， with a complete control rate of 69.7%. Repeated measures analysis of variance revealed no significant differences in symptom control based on gender or allergic rhinitis history （F = 0.575， P = 0.681； F = 2.317， P = 0.059）. There was interaction between age group and case management time. At 6-month case management， the ACT score of patients aged≥ 60 years old was significantly lower than those of patients ≤ 44 years old and 45-59 years old （both P < 0.05）. The proportion of inhaled drug usage rate and accuracy rate of using inhalation medication devices at 1-， 3-， 6- and 12-month case management was 98.8%， 86.8%， 68.0%， 51.4% and 98.8%， 100%， 100%， 100%， respectively. Conclusion The case management model can significantly improve both the usage rate and correct usage rate of inhaled medication device， enhance symptom control rate， and promote lung function recovery.

Abstract In view of the cost and reliability issues caused by mechanical position sensors, position sensorless control (PSC) of permanent magnet synchronous motors (PMSMs) has received widespread attention. A series of rotor position estimation (RPE) schemes have been developed, in which the phase‐locked loop (PLL) plays an important role. However, the conventional phase‐locked loops face two thorny issues, one is the degradation of RPE performance under motor acceleration and deceleration conditions, and the other is high noise sensitivity. To this end, an adaptive gain PLL (AG‐PLL) is proposed for PSC of PMSM drives. The gains of the loop filter in the proposed AG‐PLL are adaptively adjusted with the estimation error in order to find a compromise between the estimation performance under motor acceleration and deceleration conditions and the noise sensitivity. Moreover, the proposed scheme avoids increasing the order of the PLL, so its dynamic performance is also guaranteed. The proposed scheme is tested based on the hardware‐in‐the‐loop platform.

Abstract Background Accurate prediction of compound-protein interaction (CPI) plays a crucial role in drug discovery. Existing data-driven methods aim to learn from the chemical structures of compounds and proteins yet ignore the conceptual knowledge that is the interrelationships among the fundamental elements in the biomedical knowledge graph (KG). Knowledge graphs provide a comprehensive view of entities and relationships beyond individual compounds and proteins. They encompass a wealth of information like pathways, diseases, and biological processes, offering a richer context for CPI prediction. This contextual information can be used to identify indirect interactions, infer potential relationships, and improve prediction accuracy. In real-world applications, the prevalence of knowledge-missing compounds and proteins is a critical barrier for injecting knowledge into data-driven models. Results Here, we propose BEACON, a data and knowledge dual-driven framework that bridges chemical structure and conceptual knowledge for CPI prediction. The proposed BEACON learns the consistent representations by maximizing the mutual information between chemical structure and conceptual knowledge and predicts the missing representations by minimizing their conditional entropy. BEACON achieves state-of-the-art performance on multiple datasets compared to competing methods, notably with 5.1% and 6.6% performance gain on the BIOSNAP and DrugBank datasets, respectively. Moreover, BEACON is the only approach capable of effectively predicting knowledge representations for knowledge-lacking compounds and proteins. Conclusions Overall, our work provides a general approach for directly injecting conceptual knowledge to enhance the performance of CPI prediction.

Abstract To evaluate the effects of different delivery methods on the pelvic floor structure and function among primiparas by transperineal ultrasound, with a view to providing guidance for early postpartum intervention. Primiparas who underwent postpartum examination with transperineal ultrasound were recruited. Subjects were divided according to mode of delivery (vaginal and cesarean delivery). General information (including age, pre-pregnancy BMI and neonatal weight) were collected, and transperineal ultrasound was performed to measure such indicators as the levator hiatus areas in resting, constrictive anal and maximum Valsalva states, the posterior vesicourethral angles and distances between vesical neck and posterior inferior margin of pubic symphysis in resting and maximum Valsalva states, as well as the vesical neck mobility and urethral rotation angle in maximum Valsalva state. The inter-group differences in ultrasound indicators between the resting and Valsalva states were compared to analyze the postpartum incidences of pelvic floor dysfunctions like stress urinary incontinence, urethral funnel formation, bladder prolapse and uterine prolapse in primiparas. The levator hiatus areas in resting, constrictive anal and maximum Valsalva states were all larger in the vaginal delivery group than in the cesarean delivery group (P

Abstract Cirrhosis represents a significant global health challenge, characterized by high morbidity and mortality rates that severely impact human health. Timely and precise prognostic assessments of liver cirrhosis are crucial for improving patient outcomes and reducing mortality rates as they enable physicians to identify high-risk patients and implement early interventions. This paper features a thorough literature review on the prognostic assessment of liver cirrhosis, aiming to summarize and delineate the present status and constraints associated with the application of traditional prognostic tools in clinical settings. Among these tools, the Child–Pugh and Model for End-Stage Liver Disease (MELD) scoring systems are predominantly utilized. However, their accuracy varies significantly. These systems are generally suitable for broad assessments but lack condition-specific applicability and fail to capture the risks associated with dynamic changes in patient conditions. Future research in this field is poised for deep exploration into the integration of artificial intelligence (AI) with routine clinical and multi-omics data in patients with cirrhosis. The goal is to transition from static, unimodal assessment models to dynamic, multimodal frameworks. Such advancements will not only improve the precision of prognostic tools but also facilitate personalized medicine approaches, potentially revolutionizing clinical outcomes.

Abstract In recent years, digital dentistry has increasingly utilized advanced image analysis techniques, such as image classification and disease diagnosis, to improve clinical outcomes. Despite these advances, the lack of comprehensive benchmark datasets is a significant barrier. To address this gap, our research team develop LMCD-OR, a substantial collection of oral radiograph images designed to support extensive artificial intelligence (AI)-driven diagnostics. LMCD-OR comprises 3,818 digital imaging and communications in medicine (DICOM) oral X-ray images from local medical institutions that are meticulously annotated to provide broad category information for both primary dental outpatient services and detailed secondary disease diagnoses. This dataset is engineered to train and validate multiclassification models to improve the precision and scope of oral disease diagnostics. To ensure robust dataset validation, we employ four cutting-edge visual neural network classification models as benchmarks. These models are tested against rigorous performance metrics, demonstrating the ability of the dataset to support advanced image classification and disease diagnosis tasks. LMCD-OR is publicly available at http://dentaldataset.zeroacademy.net .

Abstract Background Breast cancer (BC) is the most common cancer in women and poses a significant health burden, especially in China. Despite advances in diagnosis and treatment, patient variability and limited early detection contribute to poor outcomes. This study examines the role of CD8 + T cells in the tumor microenvironment to identify new biomarkers that improve prognosis and guide treatment strategies. Methods CD8 + T-cell marker genes were identified using single-cell RNA sequencing (scRNA-seq), and a CD8 + T cell-related gene prognostic signature (CTRGPS) was developed using 10 machine-learning algorithms. The model was validated across seven independent public datasets from the GEO database. Clinical features and previously published signatures were also analyzed for comparison. The clinical applications of CTRGPS in biological function, immune microenvironment, and drug selection were explored, and the role of hub genes in BC progression was further investigated. Results We identified 71 CD8 + T cell-related genes and developed the CTRGPS, which demonstrated significant prognostic value, with higher risk scores linked to poorer overall survival (OS). The model’s accuracy and robustness were confirmed through Kaplan-Meier and ROC curve analyses across multiple datasets. CTRGPS outperformed existing prognostic signatures and served as an independent prognostic factor. The role of the hub gene TTK in promoting malignant proliferation and migration of BC cells was validated. Conclusion The CTRGPS enhances early diagnosis and treatment precision in BC, improving clinical outcomes. TTK, a key gene in the signature, shows promise as a therapeutic target, supporting the CTRGPS’s potential clinical utility.

Abstract Background The present study aimed to investigate the impact of the light-emitting diode (LED) green light alone or in combination with melatonin on pecking-related hormone regulation during incubation under normal and under hormonal stress conditions in breeder eggs. This study was divided into 2 experiments: In the first experiment effect of LED green light incubation on pecking-related hormones under normal conditions, on Hy-line brown (low pecking phenotype) and Roman pink (high pecking phenotype) eggs were tested. The 296 eggs of each strain were divided into two groups: LED green light incubation and dark incubation (control), each containing four replicates (37 eggs/replicate). The second experiment was conducted to evaluate the effect of LED green light incubation alone or in combination with melatonin under hormonal stress conditions on Roman pink eggs. A total of 704 Roman pink eggs were taken and divided into four groups, each consisting of 176 eggs. Each group was further divided into 2 subgroups, LED green light-regulated incubation and dark incubation with 88 eggs per subgroup, having 4 replicates of 22 eggs each. The groups were as follows: corticosterone solution injection (CI), corticosterone + melatonin mixed solution injection (CMI), Phosphate buffer solution injection (PI), and no injection (UI). Results Results of the first experiment revealed a higher level of serotonin hormone and lower corticosterone hormone in Hy-Line brown embryos compared to Roman pink embryos during dark incubation. The LED green light incubation significantly (P

Abstract Background Due to the unique working environment and nature of work in emergency departments, nurses are prone to experiencing compassion fatigue (CF), leading to job burnout and attrition. As more Generation Z (Gen Z) nurses enter the emergency department with distinct personality traits compared to previous generations, studying their experiences with CF will inform future management strategies. Methods The qualitative phenomenological research method was utilised to investigate CF among Gen Z emergency nurses at a hospital in Shanghai, China. Data were collected through face-to-face semi-structured interviews and analyzed using Colaizzi’s seven-step phenomenological analysis method. Study participants were purposively selected. Results Three main themes and nine sub-themes emerged from the study: secondary traumatic stress, including physiological symptoms, psychological symptoms, and behavioral changes; cumulative effects, including impaired empathy, interference with family life, and post-traumatic growth (PTG); coping strategies, including cognitive reconstruction, seeking support, and facilitating action. Conclusions The aim of this study is to investigate the experience of CF among Gen Z emergency nurses, providing managers with a reference for future management strategies. The significance of multi-dimensional support for Gen Z emergency nurses is underscored by our findings. Additionally, interventions that enhance resilience and competency can facilitate their psychological transformation after experiencing CF and promote accelerated personal growth.

Mycoplasma pneumoniae pneumonia （MPP） is a common cause of community-acquired pneumonia in children aged 5 and older in China. Although most affected children have favorable clinical prognosis， some cases can escalate into severe MPP due to risk factors such as pathogen resistance or co-infections， etc. These severe cases significantly jeopardize the lives of children and may lead to diverse long-term sequelae. In response to this challenge， the Children’s Medical Center of the Third Affiliated Hospital of Sun Yat-sen University， integrating current guidelines， expert consensus， and its extensive clinical experience， has devised the “412” cluster treatment strategy. This comprehensive approach encompasses treatments for anti-infection， anti-inflammatory storm， anticoagulation and antioxidant stress. Additionally， it seamlessly integrates traditional Chinese medicine with western therapies， featuring bundled respiratory management and follow-up care. This strategy provides a valuable reference for the diagnosis and treatment of severe MPP in children.

Objective: This study aimed to examine the latent profile of nurses' mental workload (MWL) and explore the influencing factors via a person-centred approach. Methods: From March to July 2023, a quantitative cross-sectional study was carried out to investigate 526 Chinese clinical nurses from five tertiary hospitals in Sichuan Province, China, by using demographic information, the Perceived Social Support Scale, Simplified Coping Skill Questionnaire, and NASA-Task Load Index. Latent profile analyses were performed using Mplus 7.3 software. Pearson’s chi-squared and logistic regression analysis was done using SPSS 24.0 software. Results: Three profiles of mental workload were identified based on the nurses’ responses to the mental workload assessment, designated as “low MWL-high self-rated (n = 70, 13.3%)”, “moderate MWL (n = 273, 51.9%)”, and “high MWL-low self-rated (n = 183, 34.8%)”. Based on the analysis of the three subtypes, nurses with working years < 5 years (χ2 = 12.135, P

Abstract Background Postoperative pulmonary complications (PPCs) after one-lung ventilation (OLV) significantly impact patient prognosis and quality of life. Objective To study the impact of an optimal inspiratory flow rate on PPCs in thoracic surgery patients. Methods One hundred eight elective thoracic surgery patients were randomly assigned to 2 groups in this consort study (control group: n = 53 with a fixed inspiratory expiratory ratio of 1:2; and experimental group [flow rate optimization group]: n = 55). Measurements of Ppeak, Pplat, PETCO2, lung dynamic compliance (Cdyn), respiratory rate, and oxygen concentration were obtained at the following specific time points: immediately after intubation (T0); immediately after starting OLV (T1); 30 min after OLV (T2); and 10 min after 2-lung ventilation (T4). The PaO2:FiO2 ratio was measured using blood gas analysis 30 min after initiating one-lung breathing (T2) and immediately when OLV ended (T3). The lung ultrasound score (LUS) was assessed following anesthesia and resuscitation (T5). The occurrence of atelectasis was documented immediately after the surgery. PPCs occurrences were noted 3 days after surgery. Results The treatment group had a significantly lower total prevalence of PPCs compared to the control group (3.64% vs. 16.98%; P = 0.022). There were no notable variations in peak airway pressure, airway plateau pressure, dynamic lung compliance, PETCO2, respiratory rate, and oxygen concentration between the two groups during intubation (T0). Dynamic lung compliance and the oxygenation index were significantly increased at T1, T2, and T4 (P

Abstract Background The period of standardized training is a transitional stage when Generation Z newly graduated registered nurses (Gen Z NGRNs) change their role from student to nurse. Affected by the COVID-19, they lack clinical practice and practicum experience in emergency departments in their university studies. At the beginning of career, they are under great pressure. Resilience is one of the factors that reduce their stress level and increases endurance. It is of interest to understand how this representative group of nurses gained and played the experience of resilience early in their careers. Objective To explore Gen Z NGRNs’ experience and process of resilience, to provide a new perspective and theoretical basis for psychological rehabilitation or intervention of medical staff who experienced transition shock. Methods This study employed a qualitative design based on the phenomenological approach. 18 nurses from a third-level class-A hospital in Shanghai who participated in standardized training in emergency department were enrolled using purposive sampling. Data collection was through in-depth and semi-structured interviews and continued until reaching data saturation. Results The investigation uncovered three themes and ten subthemes. Pressure and challenge contained high workload and high risk coexist, death’s stress response, more emergencies and high professional requirements. Coping and adaptation contained team help, psychological restructuring, peer support, transformational leadership. Reflection and planning contained enhance learning, appreciate life. Conclusions Our study described the embodiment and coping experience of the physical and mental stress faced by Gen Z NGRNs during their standardized training in the emergency department. It is emphasized that nurse educators should pay attention to the character and actual needs of Gen Z NGRNs, explore and formulate strategies, so as to guide NGRNs to quickly adapt and grow in the new role. The ultimate goal is to increase nurse retention and improve the quality of nursing.

Immune-mediated necrotizing myopathy (IMNM) with anti-HMGCR antibody positivity is characterized by proximal extremity weakness, increased creatine kinase, and extensive muscle edema. There is an urgent need to find more appropriate treatment options for anti-HMGCR IMNM patients who do not respond well to conventional therapy in the acute phase. With the advent of targeted biologics, new treatment options are available. We report on a 66-year-old anti-HMGCR IMNM patient who initially presented with a 1-month history of progressive proximal extremity weakness and dysphagia with markedly elevated creatine kinase. The patient did not respond to conventional high-dose glucocorticoid and intravenous immunoglobulin therapy, and his symptoms rapidly deteriorated over the 2 weeks after this treatment, with worsening limb weakness that prevented walking, marked proximal muscle atrophy, and weight loss. After one cycle (four infusions) of efgartigimod, the patient’s symptoms improved markedly and he has since (for several months) remained in a good clinical state.

IntroductionClassical Muscovy duck reovirus (C-MDRV) and goose-origin Muscovy duck reovirus (Go-MDRV) infections cause “Liver white-spots disease” in Muscovy duckling and gosling. It is difficult to differentiate the infections caused by C-MDRV and Go-MDRV using conventional serological methods.MethodsSpecific primers were designed and synthesized according to σNS and λA nucleotide sequences of C-MDRV and Go-MDRV, respectively. The PCR amplified products were cloned into the pMD-18-T vector. The recombinant plasmid DNA was used to establish an SYBR Green І based duplex real-time PCR assay for the simultaneous detection and differentiation of C-MDRV and Go-MDRV using high-resolution melting (HRM) analysis. The specificity, sensitivity, and repeatability of the methodology were examined based on the optimization of the reaction system and amplification conditions.ResultsC-MDRV and Go-MDRV were identified by their distinctive melting temperatures with 84.50 ± 0.25°C for C-MDRV and 87.50 ± 0.20°C for Go-MDRV, respectively. The amplifications were specific, and other non-targeted waterfowl viruses employed in this study did not show normalized melting peaks. The intra- and inter-assay coefficients of variations were between 0.05 and 1.83%, demonstrating good repeatability. The detection limits of this assay were 51.4 copies·μl−1 for C-MDRV and 61.8 copies·μl−1 for Go-MDRV, respectively. A total of 45 clinical samples were tested by RT-qPCR, with positive rates of 15.56% for C-MDRV and 22.22% for Go-MDRV, without co-infections.DiscussionThese results suggest that this duplex RT-qPCR method is highly sensitive, specific, and reproducible. The HRM assay established in this study provides a powerful tool for the differential detection and epidemiological investigation of C-MDRV and Go-MDRV.

Abstract Nuclear factor erythroid 2-related factor-2 (Nrf2) antioxidant signaling is involved in liver protection, but this generalization overlooks conflicting studies indicating that Nrf2 effects are not necessarily hepatoprotective. The role of Nrf2/heme oxygenase-1 (HO-1) in cholestatic liver injury (CLI) remains poorly defined. Here, we report that Nrf2/HO-1 activation exacerbates liver injury rather than exerting a protective effect in CLI. Inhibiting HO-1 or ameliorating bilirubin transport alleviates liver injury in CLI models. Nrf2 knockout confers hepatoprotection in CLI mice, whereas in non-CLI mice, Nrf2 knockout aggravates liver damage. In the CLI setting, oxidative stress activates Nrf2/HO-1, leads to bilirubin accumulation, and impairs mitochondrial function. High levels of bilirubin reciprocally upregulate the activation of Nrf2 and HO-1, while antioxidant and mitochondrial-targeted SOD2 overexpression attenuate bilirubin toxicity. The expression of Nrf2 and HO-1 is elevated in serum of patients with CLI. These results reveal an unrecognized function of Nrf2 signaling in exacerbating liver injury in cholestatic disease.