Search

Abstract Pullorum disease and fowl typhoid are caused by the Salmonella serovars Gallinarum biovars Pullorum and Gallinarum, respectively. The prevalence of these diseases varies across regions and is affected by different risk factors that remain insufficiently documented. To fill this knowledge gap, we have compiled a global dataset for its prevalence, drawing upon a collection of literature from the last seven decades obtained from bilingual databases. However, a more interactive and dynamic platform is still needed for both academics and policymakers to improve biosecurity measures, limit disease transmission, and prevent future outbreaks at the global and local levels. Here, we developed an advanced visualization platform to depict the prevalence of Salmonella Pullorum and Gallinarum, especially in China, which is categorized by geographical region and temporal span. The platform offers a user-friendly, efficient, and visually engaging tool to explore the prevalence of pullorum disease and fowl typhoid between 1945 and 2021 in different regions. Additionally, this platform allows users to understand the influence of various factors, such as breed, farm mode, economic usage and even the sex of the primary host, chickens, on the prevalence of this disease. We further provided a detailed overview of individual province within China. In particular, by selecting two different provinces on the interface, users can quickly visualize and grasp the disparities in disease prevalence between the chosen regions. This interactive toolkit enables a dynamic exploration of the patterns and factors contributing to the prevalence of Salmonella Pullorum and Gallinarum. This interactive platform is freely available open source at http://139.9.85.208/ .

Abstract Background Application of data-driven strategies may support veterinarians’ decision-making, benefitting pig disease prevention and control. However, little is known about veterinarians’ need for data utilisation to support their decision-making process. The current study used qualitative methods, specifically focus group discussions, to explore veterinarians’ views on data utilisation and their need for data tools in relation to pig health and welfare management in Spain, the Netherlands, and Ireland. Results Generally, veterinarians pointed out the potential benefits of using technology for pig health and welfare management, but data is not yet structurally available to support their decision-making. Veterinarians pointed out the challenge of collecting, recording, and accessing data in a consistent and timely manner. Besides, the reliability, standardisation, and the context of data were identified as important factors affecting the efficiency and effectiveness of data utilisation by veterinarians. A user-friendly, adaptable, and integrated data tool was regarded as potentially helpful for veterinarians’ daily work and supporting their decision-making. Specifically, veterinarians, particularly independent veterinary practitioners, noted a need for easy access to pig information. Veterinarians such as those working for integrated companies, corporate veterinarians, and independent veterinary practitioners expressed their need for data tools that provide useful information to monitor pig health and welfare in real-time, to visualise the prevalence of endemic disease based on a shared report between farmers, veterinarians, and other professional parties, to support decision-making, and to receive early warnings for disease prevention and control. Conclusions It is concluded that the management of pig health and welfare may benefit from data utilisation if the quality of data can be assured, the data tools can meet veterinarians’ needs for decision-making, and the collaboration of sharing data and using data between farmers, veterinarians, and other professional parties can be enhanced. Nevertheless, several notable technical and institutional barriers still exist, which need to be overcome.

Vitamin A is a micronutrient critical for versatile biological functions and has been widely used in the food, cosmetics, pharmaceutical, and nutraceutical industries. Synthetic biology and metabolic engineering enable microbes, especially the model organism Saccharomyces cerevisiae (generally recognised as safe) to possess great potential for the production of vitamin A. Herein, we first generated a vitamin A-producing strain by mining β-carotene 15,15′-mono(di)oxygenase from different sources and identified two isoenzymes Mbblh and Ssbco with comparable catalytic properties but different catalytic mechanisms. Combinational expression of isoenzymes increased the flux from β-carotene to vitamin A metabolism. To modulate the vitamin A components, retinol dehydrogenase 12 from Homo sapiens was introduced to achieve more than 90 % retinol purity using shake flask fermentation. Overexpressing POS5Δ17 enhanced the reduced nicotinamide adenine dinucleotide phosphate pool, and the titer of vitamin A was elevated by almost 46 %. Multi-copy integration of the key rate-limiting step gene Mbblh further improved the synthesis of vitamin A. Consequently, the titer of vitamin A in the strain harbouring the Ura3 marker was increased to 588 mg/L at the shake-flask level. Eventually, the highest reported titer of 5.21 g/L vitamin A in S. cerevisiae was achieved in a 1-L bioreactor. This study unlocked the potential of S. cerevisiae for synthesising vitamin A in a sustainable and economical way, laying the foundation for the commercial-scale production of bio-based vitamin A.

Abstract Purpose We aimed to evaluate the efficiency of computed tomography (CT) radiomic features extracted from gross tumor volume (GTV) and peritumoral volumes (PTV) of 5, 10, and 15 mm to identify the tumor grades corresponding to the new histological grading system proposed in 2020 by the Pathology Committee of the International Association for the Study of Lung Cancer (IASLC). Methods A total of 151 lung adenocarcinomas manifesting as pure ground-glass lung nodules (pGGNs) were included in this randomized multicenter retrospective study. Four radiomic models were constructed from GTV and GTV + 5/10/15-mm PTV, respectively, and compared. The diagnostic performance of the different models was evaluated using receiver operating characteristic curve analysis Results The pGGNs were classified into grade 1 (117), 2 (34), and 3 (0), according to the IASLC grading system. In all four radiomic models, pGGNs of grade 2 had significantly higher radiomic scores than those of grade 1 (P

BackgroundDifficulty in achieving complete resection leads to a poor prognosis for retroperitoneal soft tissue sarcoma, hence emphasizing the significance of adjuvant treatment. The benefit of preoperative radiotherapy for retroperitoneal liposarcoma was initially demonstrated by the STRASS trial. However, the impact of intraoperative radiotherapy (IORT) on retroperitoneal liposarcoma remains unexplored.MethodPatients with retroperitoneal liposarcoma were identified in the Surveillance, Epidemiology, and End Results (SEER) database, treated between 2000 and 2019. Subsequently, a 1:1 propensity score-matched (PSM) analysis was conducted based on variables identified from a multivariate analysis. T-tests were used to assess differences in normally distributed continuous variables, while the rank-sum test was applied to variables that did not follow a normal distribution. The chi-squared test was utilized to evaluate differences in categorical variables. Ultimately, survival analysis was performed using SPSS to evaluate patient prognosis.ResultA total of 2129 patients with retroperitoneal liposarcoma were included in our study. Age, sex, histology, grading, chemotherapy, and tumor size as independent prognostic risk factors for these patients through multivariate Cox regression analysis. Subsequently, 66 patients were included in the survival analysis through PSM, with 33 patients receiving IORT. Finally, the survival analysis revealed that there was no difference in overall survival among patients with retroperitoneal liposarcoma, regardless of whether they received IORT or not (p= 0.711).ConclusionAs an exploratory study, our findings suggest that patients may not derive benefit from intraoperative radiotherapy. These observations are intended to lay the groundwork for future prospective clinical studies.

Abstract Seasonal variability could have an impact on the incidence and outcome of stroke. However, little is known about the correlation between seasonal variability and location of acute cerebral infarction. This study aimed to explore the relationship between onset season and the lesions distribution of acute ischemic stroke (AIS). We retrospectively analysis data from 1488 AIS patients admitted to the Second Hospital of Tianjin Medical University from 2018 to 2022. All subjects completed head magnetic resonance imaging examination (MRI) and were divided into four groups according to the onset seasons. The lesions distribution of AIS was evaluated for anterior/posterior/double circulation infarction (DCI), unilateral/bilateral infarctions, and single/multiple cerebral infarctions based on MRI. Logistic regression models were employed to assess the association of season with lesions distribution of AIS. Subgroup analysis was performed in different stroke subtypes. Of 1488 patients, 387 (26.0%) AIS occurred in spring, 425 (28.6%) in summer, 331 (22.2%) in autumn and 345 (23.2%) in winter. Multivariate logistic regression demonstrated that the winter group had 2.15 times (95% CI:1.44–3.21) risk of multiple infarctions, 2.69 times (95% CI:1.80–4.02) of bilateral infarctions and 1.54 times (95% CI:1.05–2.26) of DCI compared with summer group, respectively. Subgroup analysis showed an increased risk of multiple (p

Abstract Background Healthcare staff in China, especially females, work in a high-pressure, high-load, and high-risk environment, which affects the physical and mental health, the efficiency and quality of work, and increases turnover intention. The present study investigated the relationship between perceived stress and turnover intention in female healthcare staff, and the effects of future-oriented coping and work-family balance on this relationship. Methods Four hundred thirty-five female medical workers were recruited to perform a perceived stress scale, future-oriented coping inventory, work-family balance scale and turnover intention scale. Meanwhile, serial multiple mediation analysis was performed using PROCESS. Results 1) Perceived stress positively predicted the level of turnover intention in female healthcare staff; 2) Preventive coping and proactive coping showed mediation effects on the relationship between perceived stress and turnover intention, and preventive coping positively related to proactive coping; 3) The work-family balance also showed mediation effects on the relationship between perceived stress and turnover intention; 4) Preventive coping, proactive coping and work-family balance showed a serial multiple mediation on the relationship between perceived stress and turnover intention in female healthcare workers. Conclusions Perceived stress affects the level of turnover intention in female healthcare staff through preventive coping, proactive coping, and work-family balance. In addition, the sequential model of future-oriented coping was validated among female healthcare staff.

Abstract Background Extrachromosomal circular DNAs (eccDNAs) have been reported to play a key role in the occurrence and development of various diseases. However, the characterization and role of eccDNAs in pulmonary arterial hypertension (PAH) remain unclear. Methods In the discovery cohort, we first explored eccDNA expression profiles by Circle-sequencing analysis. The candidate eccDNAs were validated by routine polymerase chain reaction (PCR), TOPO-TA cloning and Sanger sequencing. In the validation cohort, 30 patients with PAH and 10 healthy controls were recruited for qPCR amplification to detect the candidate eccDNAs. Datas at the baseline were collected, including clinical background, biochemical variables, echocardiography and hemodynamic factors. Receiver operating characteristic curve was used to investigate the diagnostic effect of the eccDNA. Results We identified a total of 21,741 eccDNAs in plasma samples of 3 IPAH patients and 3 individuals in good health, and the expression frequency, GC content, length distribution, and genome distribution of the eccDNAs were thoroughly characterized and analyzed. In the validation cohort, 687 eccDNAs were differentially expressed in patients with IPAH compared with healthy controls (screening threshold: |FC|≥2 and P

ABSTRACTThe United Nations has proposed Sustainable Development Goals (SDGs), of which SDG11 aims to “make cities and human settlements inclusive, safe, resilient and sustainable”. This is in line with Urban Vitality's objectives. This study proposes a quantitative framework to evaluate the impact of urban morphology on urban vitality. In this framework, a proxy that is more reflective of economic and human activities is proposed for depicting urban vitality based on geographic big data; then we use a Multi–scale Geographically Weighted Regression (MGWR) regression considering spatial heterogeneity to analyze the different effects of urban form on urban vitality. Taking the Beijing Sixth Ring Road area as the study region, the results indicate that tall, large–area, multi–functional buildings have a significantly positive impact on urban vitality. These areas exhibit single–function buildings and high levels of greenery, leading to inefficient space utilization. Increasing road network density, as well as the density of transportation and public facilities, also positively influences urban vitality. However, commercial density has a negative impact on workday vitality. Furthermore, excessive green space density, a high proportion of the largest green patches, and complex green space borders all contribute to a reduction in urban vitality.

BackgroudHuman epithelial growth factor receptor 2 (HER2) amplification is an important mechanism of acquired resistance to anti-epidermal growth factor receptor (EGFR) therapy in non-small cell lung cancer (NSCLC) patients. For patients with both EGFR mutation and HER2 amplification, there is currently no unified standard treatment, and further exploration is needed on how to choose the therapy.Methods and resultsA female NSCLC patient developed bone and brain metastases 14 and 42 months after radical surgery, respectively. The second genetic sequencing detected EGFR L858R mutation and HER2 amplification, and therefore initiated treatment with almonertinib and pyrotinib. The patient achieved partial remission and did not show any further progression during the follow-up period.ConclusionFor NSCLC patients with both EGFR mutation and HER2 amplification, the combination of almonertinib and pyrotinib is a valuable therapy that can continuously reduce tumor burden and achieve long-term survival.

This study introduces an innovative thermal management system tailored for cylindrical Lithium-ion batteries. It integrates a tubular thermoelectric generator (TTEG) to handle both battery thermal management and the utilization of waste heat for power generation. The main focus lies in evaluating the waste heat recovery capabilities of the TTEG to enhance effective thermal management. The research investigates key geometric and operational parameters such as battery discharge rate (C-rate), thermocouple count (NTC), heat transfer coefficient (HTC), and thermoelectric leg height (HL) to comprehensively assess the overall system performance. The results highlight that integrating the TTEG improves thermal management and waste heat recovery in lithium-ion batteries. It demonstrates temperature reductions of up to 21 °C at a discharge rate of 3C compared to batteries without the TTEG thermal management system. Additionally, increasing the NTC leads to a rise in the maximum battery temperature, thereby enhancing waste heat recovery performance. For instance, increasing the number of thermocouples from 100 to 144 results in a substantial up to 70 % increase in TTEG voltage.Furthermore, enhancing HTC and HL positively impacts thermal performance, showing a significant decrease in the maximum battery temperature. Elevated HTC and HL also contribute to improving thermoelectric voltage, power, and conversion efficiency, highlighting their dual role in enhancing waste heat recovery. For instance, at peak conditions, there is a notable reduction of 2.66 °C in the maximum battery temperature with an HL of 12 mm compared to 4 mm. Additionally, increasing HTC from 10 to 20, particularly at maximum discharge, leads to a surge of 15.87 % in voltage and a substantial 34.28 % increase in output power.

Small intestinal lipomatosis is a rare condition that presents a diagnostic challenge due to the absence of identifiable clinical symptoms and limitations of small intestine examination methods. Consequently, preoperative diagnosis is difficult and only a limited number of cases have been documented in the scientific literature. Here, we report a rare case of volvulus caused by small intestinal lipomatosis. A 58-year-old female patient was tentatively diagnosed with acute ileus. The whirl sign was detected using abdominal three-dimensional enhanced computed tomography, along with marked local intestinal dilation and multiple irregular fat-like containing lesions. During surgery, abnormal dilation of the small intestine between 80 and 220 cm from the ileocecal valve was detected and the affected intestine displayed a folded and twisted configuration. Examination of the resected intestine showed that the inner wall of the diseased intestinal lumen was covered with more than 100 lipomas of different sizes, the largest of which measured ~8.0 cm in diameter. Based on clinical symptoms alone, it was difficult to identify the cause of intestinal volvulus before surgery. Complete resection of the affected small intestine and subsequent pathological analysis yielded a definitive diagnosis of small intestinal lipomatosis. While small intestinal lipomatosis is a rare condition, prognosis is favorable if diagnosed early and treated appropriately. The application of three-dimensional enhanced computed tomography imaging can aid in accurate diagnosis, while complete resection of the affected small intestine is crucial to improve patient prognosis.

Fluorescence-activated cell sorting (FACS) holds great promise for the separation of single cells or cell populations according to specific light scattering and fluorescent characteristics. Here, we present a new perspective on microfluidic FACS (μFACS) with predictable geometry, which meets the requirements of high-throughput analysis and sorting. Instead of the widely applied elastic polydimethylsiloxane (PDMS), a rigid epoxy resin chip was rapidly fabricated and irreversibly encapsulated to eliminate channel deformation (tenfold reduction) and enhance performance while meeting high pressure (>600 kPa) and high flow rate application scenarios. Fluorescence discrimination and particle differentiation were additionally validated in a self-contained μFACS system using calibration microspheres and mammalian cells. The μFACS chip and system were integrally optimized to achieve a minimum interval (0.58 ms) with a mean flow rate of 1.5 m/s. Ultimately, event recording and automated sorting were accomplished in real time while achieving a sorting efficiency of 87% at cell throughput of 8,000 events/s. This rigid chip for high-throughput μFACS, which is independent of the physical properties of cells could pave the way for cell screening in plasma samples for personalized medicine.

Abstract Infectious disease outbreaks transcend the medical and public health realms, triggering widespread panic and impeding socio-economic development. Considering that self-limiting diarrhoea of sporadic cases is usually underreported, the Salmonella outbreak (SO) study offers a unique opportunity for source tracing, spatiotemporal correlation, and outbreak prediction. To summarize the pattern of SO and estimate observational epidemiological indicators, 1,134 qualitative reports screened from 1949 to 2023 were included in the systematic review dataset, which contained a 506-study meta-analysis dataset. In addition to the dataset comprising over 50 columns with a total of 46,494 entries eligible for inclusion in systematic reviews or input into prediction models, we also provide initial literature collection datasets and datasets containing socio-economic and climate information for relevant regions. This study has a broad impact on advancing knowledge regarding epidemic trends and prevention priorities in diverse salmonellosis outbreaks and guiding rational policy-making or predictive modeling to mitigate the infringement upon the right to life imposed by significant epidemics.

This article investigates the safety risks associated with sea ice along the Arctic Route by using polar sea ice images obtained by Gaofen-3 (GF3) Synthetic Aperture Radar (SAR) satellites. We collected three SAR datasets, representative of GF3 satellites' operational modes, and constructed semantic segmentation datasets through meticulous annotation using concurrent optical satellite imagery obtained by Landsat satellites to ensure precision. We propose SI-CTFNet, an innovative sea ice semantic segmentation model that integrates convolutional neural networks (CNNs) and vision transformers (ViTs) for enhanced multiscale feature extraction. SI-CTFNet features a dual-pathway architecture designed to optimize feature extraction, complemented by the BiAFusion module, which effectively merges local and global features to improve decoding accuracy. In addition, we introduce the MSDAM module to facilitate a comprehensive multiscale contextual analysis, addressing the diverse distribution of ice types in the imagery. The model incorporates advanced decoding techniques, including a progressive upsampling approach for the CNN-fusion branch and an efficient All-MLP module for the Transformer branch. Performance evaluations across three distinct datasets reveal that SI-CTFNet significantly outperforms existing methods in key metrics and maintains efficacy with supplementary Sentinel1A C-band satellite data. Furthermore, we present a streamlined variant of SI-CTFNet, which achieves a threefold increase in inference speed with minimal reduction in classification accuracy. The ultimate objective of this work is to advance a precise sea ice forecasting and navigation system for polar regions, aimed at automating sea ice classification within a smart polar shipping framework.

Abstract Direct electrosynthesis of hydrogen peroxide (H2O2) via the two-electron oxygen reduction reaction presents a burgeoning alternative to the conventional energy-intensive anthraquinone process for on-site applications. Nevertheless, its adoption is currently hindered by inferior H2O2 selectivity and diminished H2O2 yield induced by consecutive H2O2 reduction or Fenton reactions. Herein, guided by theoretical calculations, we endeavor to overcome this challenge by activating a main-group Pb single-atom catalyst via a local micro-environment engineering strategy employing a sulfur and oxygen super-coordinated structure. The main-group catalyst, synthesized using a carbon dot-assisted pyrolysis technique, displays an industrial current density reaching 400 mA cm−2 and elevated accumulated H2O2 concentrations (1358 mM) with remarkable Faradaic efficiencies. Both experimental results and theoretical simulations elucidate that S and O super-coordination directs a fraction of electrons from the main-group Pb sites to the coordinated oxygen atoms, consequently optimizing the *OOH binding energy and augmenting the 2e− oxygen reduction activity. This work unveils novel avenues for mitigating the production-depletion challenge in H2O2 electrosynthesis through the rational design of main-group catalysts.

Abstract Palm leaf manuscripts (PLMs) are of great importance in recording Buddhist Scriptures, medicine, history, philosophy, etc. Some damages occur during the use, spread, and preservation procedure. The comprehensive investigation of Sanskrit PLMs is a prerequisite for further conservation and restoration. However, current damage identification and investigation are carried out manually. They require strong professional skills and are extraordinarily time-consuming. In this study, PLM-SegFormer is developed to provide an automated damage segmentation for Sanskrit PLMs based on the SegFormer architecture. Firstly, a digital image dataset of Sanskrit PLMs (the PLM dataset) was obtained from the Potala Palace in Tibet. Then, the hyperparameters for pre-processing, model training, prediction, and post-processing phases were fully optimized to make the SegFormer model more suitable for the PLM damage segmentation task. The optimized segmentation model reaches 70.1% mHit and 51.2% mIoU. The proposed framework automates the damage segmentation of 10,064 folios of PLMs within 12 h. The PLM-SegFormer framework will facilitate the preservation state survey and record of the Palm-leaf manuscript and be of great value to the subsequent preservation and restoration. The source code is available at https://github.com/Ryan21wy/PLM_SegFormer .

Game theory-inspired deep learning using a generative adversarial network provides an environment to competitively interact and accomplish a goal. In the context of medical imaging, most work has focused on achieving single tasks such as improving image resolution, segmenting images, and correcting motion artifacts. We developed a dual-objective adversarial learning framework that simultaneously 1) reconstructs higher quality brain magnetic resonance images (MRIs) that 2) retain disease-specific imaging features critical for predicting progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD). We obtained 3-Tesla, T1-weighted brain MRIs of participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, N=342) and the National Alzheimer’s Coordinating Center (NACC, N =190) datasets. We simulated MRIs with missing data by removing 50% of sagittal slices from the original scans (i.e., diced scans). The generator was trained to reconstruct brain MRIs using the diced scans as input. We introduced a classifier into the GAN architecture to discriminate between stable (i.e., sMCI) and progressive MCI (i.e., pMCI) based on the generated images to facilitate encoding of disease-related information during reconstruction. The framework was trained using ADNI data and externally validated on NACC data. In the NACC cohort, generated images had better image quality than the diced scans (Structural similarity (SSIM) index: $0.553 \pm 0.116$ versus $0.348 \pm 0.108$ ). Furthermore, a classifier utilizing the generated images distinguished pMCI from sMCI more accurately than with the diced scans (F1-score: $0.634 \pm 0.019$ versus $0.573 \pm 0.028$ ). Competitive deep learning has potential to facilitate disease-oriented image reconstruction in those at risk of developing Alzheimer’s disease.

Radar echo image sequence prediction is a spatial-temporal forecasting problem, which is one of the main challenges in precipitation nowcasting. In response to issues such as poor extraction of spatiotemporal features and blurred prediction of image sequence in previous models, this paper proposes a spatial-temporal dual discriminator precipitation nowcasting method using SNGAN with InceptionGRU (STD-SNGAN-InceptionGRU). The design of temporal and spatial dual discriminators constrains the generator’s prediction samples, effectively enhancing the spatiotemporal prediction capability of the generator through adversarial training. The generator utilizes multiscale convolution (Inception) and multiscale gate recurrent unit (InceptionGRU) to enhance its ability to extract spatiotemporal features, thereby improving its learning capability for the evolution of radar echo sequences. The comparative experimental results demonstrate that the proposed STD-SNGAN-InceptionGRU model outperforms other algorithms in terms of critical success index (CSI) and probability of detection (POD) at different echo intensities, especially in areas with long time series and high echo intensity. Furthermore, through ablation experiments, this paper proves that the temporal discriminator, spatial discriminator, and InceptionGRU all effectively improve the model’s prediction capability for radar echo images.

BackgroundChronic excessive exposure to fluoride can cause damage to the central nervous system and a certain degree of learning and memory impairment. However, the associated mechanism is not yet clear and further exploration is needed. ObjectiveUsing 4D unlabelled quantitative proteomics techniques to explore differentially expressed proteins and their potential mechanisms of action in chronic excessive fluoride exposure induced brain injury. MethodsTwenty-four SPF-grade adult SD rats, half male and half male, were selected and divided into a control group and a fluoride group by random number table method, with 12 rats in each group. Among them, the control group drank tap water (fluorine content

Although serum albumin and neuroticism have revealed a strong association with suicidal ideation in individuals with depression, the causal relationship between them is uncertain. This study analyzed the causal association of serum albumin, neuroticism and suicidal ideation using large-scale GWAS data and Univariable Mendelian Randomization (UVMR) methods. The Multivariable MR (MVMR) analysis was used to explore the causal pathways. UVMR analysis revealed that genetically determined serum albumin is causally associated with neuroticism (β = −0.006 S.D.; 95 % CI: 0.009, −0.002; p = 0.003) and suicidal ideation (β = 0.009 S.D.; 95 % CI: 0.001, 0.016; p = 0.037); and that neuroticism mediates 100 % of the causal association between serum albumin and suicidal ideation in individuals with depression. These findings suggest genetic evidence for the causal effect of serum albumin on suicidal ideation in depressed patients and the significant mediation effect of neuroticism on this causal association. This study proves the protective role of serum albumin for neuroticism and the riskiness of personality traits for suicidal ideation in individuals with depression.

IntroductionPsoriasis is a chronic skin disease characterized by unique scaling plaques. However, during the acute phase, psoriatic lesions exhibit eczematous changes, making them difficult to distinguish from atopic dermatitis, which poses challenges for the selection of biological agents. This study aimed to identify potential diagnostic genes in psoriatic lesions and investigate their clinical significance.MethodsGSE182740 datasets from the GEO database were analyzed for differential analysis; machine learning algorithms (SVM-RFE and LASSO regression models) are used to screen for diagnostic markers; CIBERSORTx is used to determine the dynamic changes of 22 different immune cell components in normal skin lesions, psoriatic non-lesional skin, and psoriatic lesional skin, as well as the expression of the diagnostic genes in 10 major immune cells, and real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry are used to validate results.ResultsWe obtained 580 differentially expressed genes (DEGs) in the skin lesion and non-lesion of psoriasis patients, 813 DEGs in mixed patients between non-lesions and lesions, and 96 DEGs in the skin lesion and non-lesion of atopic dermatitis, respectively. Then 144 specific DEGs in psoriasis via a Veen diagram were identified. Ultimately, UGGT1, CCNE1, MMP9 and ARHGEF28 are identified for potential diagnostic genes from these 144 specific DEGs. The value of the selected diagnostic genes was verified by receiver operating characteristic (ROC) curves with expanded samples. The the area under the ROC curve (AUC) exceeded 0.7 for the four diagnosis genes. RT-qPCR results showed that compared to normal human epidermis, the expression of UGGT1, CCNE1, and MMP9 was significantly increased in patients with psoriasis, while ARHGEF28 expression was significantly decreased. Notably, the results of CIBERSORTx showed that CCNE1 was highly expressed in CD4+ T cells and neutrophils, ARHGEF28 was also expressed in mast cells. Additionally, CCNE1 was strongly correlated with IL-17/CXCL8/9/10 and CCL20. Immunohistochemical results showed increased nuclear expression of CCNE1 in psoriatic epidermal cells relative to normal.ConclusionBased on the performance of the four genes in ROC curves and their expression in immune cells from patients with psoriasis, we suggest that CCNE1 possess higher diagnostic value.

Ageing is an inevitable process that affects various tissues and organs of the human body, leading to a series of physiological and pathological changes. Mechanisms such as telomere depletion, stem cell depletion, macrophage dysfunction, and cellular senescence gradually manifest in the body, significantly increasing the incidence of diseases in elderly individuals. These mechanisms interact with each other, profoundly impacting the quality of life of older adults. As the ageing population continues to grow, the burden on the public health system is expected to intensify. Globally, the prevalence of musculoskeletal system diseases in elderly individuals is increasing, resulting in reduced limb mobility and prolonged suffering. This review aims to elucidate the mechanisms of ageing and their interplay while exploring their impact on diseases such as osteoarthritis, osteoporosis, and sarcopenia. By delving into the mechanisms of ageing, further research can be conducted to prevent and mitigate its effects, with the ultimate goal of alleviating the suffering of elderly patients in the future.

Purpose:Sacroiliac joint dysfunction (SIJD), while being the primary contributor to low back pain, is still disregarded and treated as low back pain. Mulligan’s Mobilization with Movement (MWM) Techniques and Core Stability Exercises (CSE) are often used to treat low back pain. There is not much evidence that it is effective in SIJD. To evaluate the effectiveness of CSE coupled with MWM (CSE + MWM) in the treatment of SIJD.Methods:39 patients with SIJD were recruited and randomly divided into distinct groups as follows: control group (n = 13), CSE group (n = 13) and CSE + MWM group (n = 13). The Numerical Pain Rating Scale (NPRS), the Roland Morris Disability Questionnaire (RMDQ), the Range of Motion (ROM), the Pressure Pain Threshold (PPT) and the pelvic tilt angle asymmetry ratio in the sagittal plane (PTAR) were used to gauge the intervention’s success both before (M0) and after (M1) it. All experimental data were statistically analyzed.Results:The SIJ-related pain metric significantly decreased in both the CSE + MWM group and the CSE group between M0 and M1, as determined by the NPRS and RMDQ. Between M0 and M1, The CSE group’s left axial rotation ROM and lumbar flexion ROM were significantly decreased. The CSE + MWM group’s extension ROM and left lateral flexion ROM both significantly increased between M0 and M1. In the difference variable (M1-M0), the CSE + MWM group substantially outperformed control group in the left lateral flexion ROM and outperformed the CSE group in the left axial rotation ROM.Conclusion:In individuals with SIJD, CSE + MWM is beneficial in lowering pain, disability, and function. Treatment with CSE and MWM approaches for SIJ appears to boost this efficacy.

BackgroundResident physicians at the standardized training stage had undergone significant physical and mental stress during the release of the COVID-19 pandemic restrictions at the end of 2022 in China. This study aimed to investigate the psychological status (including anxiety, depression, somatic symptoms, job burnout, and vicarious trauma) of resident physicians and identify its influencing factors under these special periods.MethodsSurvey was conducted one month after the release of the COVID-19 pandemic restrictions on resident training physicians from a tertiary first-class hospital in Zhejiang, China. Resident physicians completed the psychological status questionnaire. Chi-square tests, Mann–Whitney U tests, and logistic regression analyses were used to estimate the group differences and variable associations.ResultsThe prevalence of anxiety, depression, and somatic discomfort in this study was 20.88, 28.53, and 41.47%, respectively. Female resident physicians were more likely to experience somatic symptoms [adjusted odds ratio (OR) = 2.36, 95% confidence interval (CI): 1.33–4.18]. Resident physicians with problem-focused coping styles were less prone to psychological health issues [depression (adjusted OR = 0.92, 95% CI: 0.88–0.96), anxiety (adjusted OR = 0.94, 95% CI: 0.90–0.98), somatic symptoms (adjusted OR = 0.93, 95% CI: 0.89–0.97), job burnout (adjusted OR = 0.91, 95% CI: 0.87–0.96) and vicarious trauma (adjusted OR = 0.94, 95% CI: 0.90–0.98)]. Inversely, resident physicians with emotion-focused coping styles and experienced negative life events were more prone to psychological health issues.ConclusionResident training physicians had a high risk of anxiety, depression, and somatic symptoms under the special COVID-19 pandemic restriction release period. Females, with lower training stages, degrees, negative life events, and emotion-focused coping styles had a disadvantaged effect on psychological status. The medical teaching management department needs to monitor and reduce the workload and working hours of resident physicians, ensure sufficient sleep time, and pay attention to the psychological status of resident physicians. By strengthening regular communication and mental health education or intervention, which can help them improve their ability to cope with complex tasks.

During malignant tumour development, the extracellular matrix (ECM) is usually abnormally regulated. Dysregulated expression of lysyl oxidase‐like 2 (LOXL2), matrix metalloproteinase 9 (MMP9) and lipocalin 2 (LCN2) are associated with ECM remodelling. In this study, protein–protein interaction assays indicated that LCN2 and LOXL2 interactions and LCN2 and MMP9 interactions occurred both intracellularly and extracellularly, but interactions between LOXL2 and MMP9 only occurred intracellularly. The LCN2/LOXL2/MMP9 ternary complex promoted migration and invasion of oesophageal squamous cell carcinoma (ESCC) cells, as well as tumour growth and malignant progression in vivo, while the iron chelator deferoxamine mesylate (DFOM) inhibited ESCC tumour growth. Co‐overexpression of LCN2, LOXL2 and MMP9 enhanced the ability of tumour cells to degrade fibronectin and Matrigel, increased the formation and extension of filopodia, and promoted the rearrangement of microfilaments through upregulation of profilin 1. In addition, the LCN2/LOXL2/MMP9 ternary complex promoted the expression of testican‐1 (SPOCK1), and abnormally activated the FAK/AKT/GSK3β signalling pathway. In summary, the LCN2/LOXL2/MMP9 ternary complex promoted the migration and invasion of cancer cells and malignant tumour progression through multiple mechanisms and could be a potential therapeutic target.

Abstract Salmonella enterica has a wide diversity, with numerous serovars belonging to six different subspecies with dynamic animal-host tropism. The FimH protein is the adhesin mediating binding to various cells, and slight amino acid discrepancy significantly affects the adherence capacities. To date, the general function of FimH variability across different subspecies of Salmonella enterica has not been addressed. To investigate the biological functions of FimH among the six Salmonella enterica subspecies, the present study performed several assays to determine biofilm formation, Caenorhabditis elegans killing, and intestinal porcine enterocyte cell IPEC-J2 adhesion by using various FimH allele mutants. In general, allelic mutations in both the lectin and pilin domains of FimH could cause changes in binding affinity, such as the N79S mutation. We also observed that the N79S variation in Salmonella Dublin increased the adhesive ability of IPEC-J2 cells. Moreover, a new amino acid substitution, T260M, within the pilin domain in one subspecies IIIb strain beneficial to binding to cells was highlighted in this study, even though the biofilm-forming and Caenorhabditis elegans-killing abilities exhibited no significant differences in variants. Combined with point mutations being a natural tendency due to positive selection in harsh environments, we speculate that allelic variation T260M probably contributes to pathoadaptive evolution in Salmonella enterica subspecies IIIb.

To investigate the effect of cyclic boiling of the brine pack on the flavor of brine fried anchovy, the electronic tongue was used to investigate the difference in taste between the sample A (fried anchovy brined in the first-use brine) and sample B (fried anchovy brined in brine after recycling for 5 times). The volatile compounds in samples A and B were analyzed by electronic nose and HS-SPME-GC-MS. The results showed that, in the E-tongue, the taste of sample B was stronger in umami, bitterness and saltiness than sample A. For the E-nose, the differences between samples A and B were mainly in the W2W (organic sulfurs), W1S (methane short chain alkane gas) and W1W (sulfide) sensors, with the first two decreasing but W1W (sulfide) increasing in sample B. A total of 62 volatile substances were detected by GC-MS: 45 substances were detected in sample A and 54 substances were detected in sample B. Thirty-six substances were detected in both samples, among which the relatively high content of main flavor substances including linalool, trans-anethole, linalool acetate and 4-allyl anisole were higher content in fried anchovy B. In summary, the cyclic boiling of brine was beneficial in promoting the production of beneficial volatile flavors in fried anchovy and the expression of volatile flavor components of the spices. This would provide the theoretical support for technical optimization of brine fried anchovy.

The Sapindus saponaria (soapberry) kernel is rich in oil that has antibacterial, anti-inflammatory, and antioxidant properties, promotes cell proliferation, cell migration, and stimulates skin wound-healing effects. S. saponaria oil has excellent lubricating properties and is a high-quality raw material for biodiesel and premium lubricants, showing great potential in industrial and medical applications. Metabolite and transcriptome analysis revealed patterns of oil accumulation and composition and differentially expressed genes (DEGs) during seed development. Morphological observations of soapberry fruits at different developmental stages were conducted, and the oil content and fatty acid composition of the kernels were determined. Transcriptome sequencing was performed on kernels at 70, 100, and 130 days after flowering (DAF). The oil content of soapberry kernels was lowest at 60 DAF (5%) and peaked at 130 DAF (31%). Following soapberry fruit-ripening, the primary fatty acids in the kernels were C18:1 (oleic acid) and C18:3 (linolenic acid), accounting for an average proportion of 62% and 18%, respectively. The average contents of unsaturated fatty acids and saturated fatty acids in the kernel were 86% and 14%, respectively. Through the dynamic changes in fatty acid composition and DEGs analysis of soapberry kernels, FATA, KCR1, ECR, FAD2 and FAD3 were identified as candidate genes contributing to a high proportion of C18:1 and C18:3, while DGAT3 emerged as a key candidate gene for TAG biosynthesis. The combined analysis of transcriptome and metabolism unveiled the molecular mechanism of oil accumulation, leading to the creation of a metabolic pathway pattern diagram for oil biosynthesis in S. saponaria kernels. The study of soapberry fruit development, kernel oil accumulation, and the molecular mechanism of oil biosynthesis holds great significance in increasing oil yield and improving oil quality.

Based on the analysis of the problems in material distribution routes, we propose the idea of integrating the intelligent connected vehicle system with material distribution, and construct an intelligent connected vehicle material distribution route model based on k-center spatial cellular clustering and an improved cockroach optimization algorithm. Firstly, we set the research scope to include the distribution center, the distribution points and the geographical environment. A cellular spatial model of distribution points is constructed to quantify and visualize the neighborhood relationship between the distribution centers and distribution points. On this basis, we construct an intelligent connected vehicle material distribution route model based on the improved cockroach optimization algorithm, and the optimal material distribution center is determined by searching for the corresponding optimal distribution route of each distribution center. In the experiment, we use the concept of symmetry to design routes that start from the initial points. The route passes through the distribution point, and finally reaches the destination. In this mode, the experiment generates symmetrically round-trip routes and generates different distribution time schedules. Case studies and comparative experiments show that the proposed algorithm has a total distance cost 1.2 km lower than the distance cost generated by the Baidu Map method and 2.7 km lower than the distance cost generated by the 360 Map method. In terms of the total time cost of the proposed algorithm, it is 0.06 h lower than the time cost generated by the Baidu Map method and 0.135 h lower than the time cost generated by the 360 Map method. Compared with the commonly used Dijkstra algorithm and the A* algorithm for route optimization, our proposed algorithm also generates a lower cost than the two other types of optimization algorithms. In the case study, the distance generated by the proposed algorithm is 1.8 km lower than that of the Dijkstra algorithm, and the total time cost is 0.09 h lower than that of the Dijkstra algorithm. The distance generated by the proposed algorithm is 1.6 km lower than that of the A* algorithm, and the total time cost is 0.08 h lower than that of the A* algorithm. Meanwhile, the proposed algorithm has a lower time complexity than the two commonly used optimization algorithms. Therefore, our proposed algorithm can find the distribution route with the lowest transportation cost. Compared to the commonly used electronic maps and the optimization algorithms for distribution route planning, our proposed algorithm can output distribution routes with lower costs under the same distribution sequence, and reduce the transportation costs for intelligent connected vehicle material distribution systems to the maximum extent.

This paper presents a comprehensive investigation into the thermal stability of superlattice-like (SLL) thin films fabricated by varying the sputtering sequences of the SLL [Ge8Sb92(25nm)/GeTe(25nm)]1 and SLL [GeTe(25nm)/Ge8Sb92(25nm)]1 configurations. Our results reveal significantly enhanced ten-year data retention (Tten) for both thin films measured at 124.3 °C and 151.9 °C, respectively. These values surpass the Tten of Ge2Sb2Te5 (85 °C), clearly demonstrating the superior thermal stability of the studied SLL configurations. Interestingly, we also observe a distinct difference in the thermal stability between the two SLL configurations. The superior thermal stability of SLL [GeTe(25nm)/Ge8Sb92(25nm)]1 is attributed to the diffusion of the Sb precipitated phase from Ge8Sb92 to GeTe. This diffusion process effectively reduces the impact of the Sb phase on the thermal stability of the thin film. In contrast, in the case of SLL [Ge8Sb92(25nm)/GeTe(25nm)]1, the presence of the Sb precipitated phase in Ge8Sb92 facilitates the crystallization of GeTe, leading to reduced thermal stability. These findings underscore the significant influence of the sputtering sequence on the atomic behavior and thermal properties of superlattice-like phase change materials. Such insights provide a robust foundation for the design and exploration of novel phase change materials with improved thermal performance.

Abstract Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. Many hot issues can be investigated in desert ecosystems, including the relationship between species distribution and environmental gradients (e.g., elevation, slope, topographic convergence index, and solar insolation). Species Distribution Models (SDMs) can be used to understand these relationships. We used data acquired from the important desert plant Nitraria tangutorum Bobr. communities and desert topographic factors extracted from LiDAR (Light Detection and Ranging) data of one square kilometer in the inner Mongolia region of China to develop SDMs. We evaluated the performance of SDMs developed with a variety of both the parametric and nonparametric algorithms (Bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, Generalized Linear Model, Generalized Additive Model, Random Forest (RF), and Support Vector Machine). The area under the receiver operating characteristic curve was used to evaluate these algorithms. The SDMs developed with RF showed the best performance based on the area under curve (0.7733). We also produced the Nitraria tangutorum Bobr. distribution maps with the best SDM and suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to southern part with 0–20 degree slopes at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on the desert species distribution on a small scale. The presented SDMs can have important applications for predicting species distribution and will be useful for preparing conservation and management strategies for desert ecosystems on a small scale.

Abstract Background Abrus cantoniensis Hance. (Ac) and Abrus mollis (Am), two edible and medicinal plants with economic value in southern China, belong to the Abrus genus. Due to its growth characteristics, Am often replaces Ac in folk medicine. However, the latest National Pharmacopeia of China only recommends Ac. The differences in the metabolite composition of the plants are directly related to the differences in their clinical efficacy. Results The difference in metabolites were analyzed using an untargeted metabolomic approach based on ultrahigh-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC‒ESI‒MS/MS). The roots (R), stems (S) and leaves (L) of the two varieties were examined, and 635 metabolites belonging to 8 classes were detected. A comparative study revealed clear variations in the metabolic profiles of the two plants, and the AmR group had more active ingredients (flavonoids and terpenoids) than the AcR group. The metabolites classified as flavonoids and triterpene saponins showed considerable variations among the various samples. Both Ac and Am had unique metabolites. Two metabolites (isovitexin-2''-xyloside and soyasaponin V) specifically belong to Ac, and nine metabolites (vitexin-2"-O-galactoside, ethyl salicylate, 6-acetamidohexanoic acid, rhein-8-O-glucoside, hederagenin-3-O-glucuronide-28-O-glucosyl(1,2)-glucoside, methyl dioxindole-3-acetate, veratric acid, isorhamnetin-3-O-sophoroside-7-O-rhamnoside, and isorhamnetin-3-O-sophoroside) specifically belong to Am. Conclusions The metabolite differences between Ac and Am cause the differences in their clinical efficacy. Our findings serve as a foundation for further investigation of biosynthesis pathways and associated bioactivities and provide guidance for the clinical application of traditional Chinese medicine.

Abstract Background The coronavirus disease (COVID‐19) and universal mitigation strategies have fundamentally affected peoples’ lives worldwide, particularly during the first two years of the pandemic. Reductions in physical activity (PA) and increased mental health (MH) problems among children and youth have been observed. This systematic review and meta-analysis investigated the relationship between physical activity (PA) and mental health (MH) among children and youth during the COVID‐19 pandemic. Methods Four electronic databases (EMBASE, PsycINFO, PubMed, and Web of Science) were systematically searched to identify studies that (1) examined the relationship between PA and MH among children and youth (aged 2–24 years old) and (2) were published in peer-reviewed journals in English between January 2020 and December 2021. Relationships between PA and two MH aspects (i.e., negative and positive psychological responses) among children and youth at different age ranges and those with disabilities or chronic conditions (DCC) were synthesized. Meta-analyses were also performed for eligible studies to determine the pooled effect size. Results A total of 58 studies were eventually included for variable categorization, with 32 eligible for meta-analyses. Our synthesis results showed that greater PA participation was strongly related to lower negative psychological responses (i.e., anxiety, depression, stress, insomnia, fatigue, and mental health problems) and higher positive psychological responses (i.e., general well-being and vigor) in children and youth during COVID-19. The pattern and strength of relations between PA and MH outcomes varied across age ranges and health conditions, with preschoolers and those with DCC receiving less attention in the existing research. Meta-analysis results showed that the magnitude of associations of PA with negative (Fisher’s z = − 0.198, p

Abstract Pentamethyl cyanine dyes are promising fluorophores for fluorescence sensing and imaging. However, advanced biomedical applications require enhanced control of their excited‐state properties. Herein, a synthetic approach for attaching aryl substituents at the C2’ position of the thio‐pentamethine cyanine (TCy5) dye structure is reported for the first time. C2’‐aryl substitution enables the regulation of both the twisted intramolecular charge transfer (TICT) and photoinduced electron transfer (PET) mechanisms to be regulated in the excited state. Modulation of these mechanisms allows the design of a nitroreductase‐activatable TCy5 fluorophore for hypoxic tumor photodynamic therapy and fluorescence imaging. These C2’‐aryl TCy5 dyes provide a tunable platform for engineering cyanine dyes tailored to sophisticated biological applications, such as photodynamic therapy.

The Internet of Things (IoT) is gradually changing the way teaching and learning take place in on-campus programs. In particular, face capture services improve student concentration to create an efficient classroom atmosphere by using face recognition algorithms that support end devices. However, reducing response latency and executing face analysis services effectively in real-time is still challenging. For this reason, this paper proposed a pedagogical model of face recognition for IoT devices based on edge computing (TFREC). Specifically, this research first proposed an IoT service-based face capture algorithm to optimize the accuracy of face recognition. In addition, the service deployment method based on edge computing is proposed in this paper to obtain the best deployment strategy and reduce the latency of the algorithm. Finally, the comparative experimental results demonstrate that TFREC has 98.3% accuracy in face recognition and 72 milliseconds in terms of service response time. This research is significant for advancing the optimization of teaching methods in school-based courses, meanwhile, providing beneficial insights for the application of face recognition and edge computing in the field of education.

The evolution of the automobile market is a macro-expression of the behavior of automakers' production decisions. This study examines the competitive environment between new energy vehicles (NEVs) and conventional fuel vehicles (CFVs) and develops a game-theoretical model incorporating consumer utility, automaker profit, and the competitive density of NEVs and CFVs. It aims to assess how consumers' preferences for vehicle range and smart features influence automakers' strategic decisions and the broader market evolution under the Dual Credit Policy. The findings indicate: (1) A low NEV credit price facilitates NEV market size growth, but this growth rate diminishes beyond a certain price threshold; (2) The lower the consumer's range preference, the higher NEV credit price can accelerate the development of new energy vehicles to their saturation value. However, when consumers in the market prioritize smart features, increasing the NEV credit price does not significantly influence the growth of NEV market size. (3) Higher consumer preferences for both range and smart features, combined with increased NEV credit prices, can synergistically accelerate the speed of the NEV market to reach the saturation value and also raise the saturation value of the scale of NEVs. And higher consumer range preference combined with increased NEV credit prices has a more significant effect on the promotion of NEV market size than the combined effect of higher consumer smart preference and increased NEV credit prices. The actual data of China's automobile market is used in the simulation of this model. The model and its simulation results effectively explain and reveal the evolutionary impacts of consumers' range and smart feature preference on the promotion of China's NEVs under the Dual Credit Policy to provide effective technological and theoretical support for the promotion of the sustainable development of China's NEV industry.

ABSTRACT: Hydrogen sulfide (H2S) is one of the most irritant gases present in rearing stalls that suppress broilers’ healthy growth, which is seriously required an effective alleviation method. In this study, Lactobacillus was supplemented to investigate the alleviative effects on broilers reared under consecutive H2S exposure. A total of 180 healthy 1-day-old male AA broilers with similar body weight (40.8 ± 1.0 g) were randomly allotted into the control treatment (CON), the hydrogen sulfide treatment (H2S), and the Lactobacillus supplement under H2S exposure treatment (LAC) for a 42-d-long feeding process. Growth and carcass performances, immunity-related parameters, intestinal development and cecal microbial communities, and blood metabolites were measured. Results showed that Lactobacillus supplement significantly increased the body weight gain (BWG) while reduced the mortality rate, abdominal fat and bursa of fabricius weight during the whole rearing time compared with H2S treatment (P < 0.05). Serum LPS, IL-1β, IL-2, and IL-6 contents were observed significantly increased after H2S treatment while remarkably decreased after Lactobacillus supplementation(P < 0.05). Intestinal morphology results showed a significant higher in the development of ileum villus height (P < 0.05). Cecal microbiota results showed the bacterial composition was significantly altered after Lactobacillus supplement (P < 0.05). Specifically, Lactobacillus supplement significantly decreased the relative abundance of Faecalibacterium, while significantly proliferated the relative abundance of Lactobacillus, Bifidobacterium, Clostridium, and Campylobacter (P

Abstract Cardiovascular diseases, such as myocardial infarction (MI), are a leading cause of death worldwide. Acute MI (AMI) inflicts massive injury to the coronary microcirculation, causing large-scale cardiomyocyte death due to ischemia and hypoxia. Inflammatory cells such as monocytes and macrophages migrate to the damaged area to clear away dead cells post-MI. Macrophages are pleiotropic cells of the innate immune system, which play an essential role in the initial inflammatory response that occurs following MI, inducing subsequent damage and facilitating recovery. Besides their recognized role within the immune response, macrophages participate in crosstalk with other cells (including cardiomyocytes, fibroblasts, immune cells, and vascular endothelial cells) to coordinate post-MI processes within cardiac tissue. Macrophage-secreted exosomes have recently attracted increasing attention, which has led to a more elaborate understanding of macrophage function. Currently, the functional roles of macrophages in the microenvironment of the infarcted heart, particularly with regard to their interaction with surrounding cells, remain unclear. Understanding the specific mechanisms that mediate this crosstalk is essential in treating MI. In this review, we discuss the origin of macrophages, changes in their distribution post-MI, phenotypic and functional plasticity, as well as the specific signaling pathways involved, with a focus on the crosstalk with other cells in the heart. Thus, we provide a new perspective on the treatment of MI. Further in-depth research is required to elucidate the mechanisms underlying crosstalk between macrophages and other cells within cardiac tissue for the identification of potential therapeutic targets. Video Abstract

Abstract We used pyroptosis-related genes to establish a risk–score model for prognostic prediction of liver hepatocellular carcinoma (LIHC) patients. A total of 52 pyroptosis-associated genes were identified. Then, data for 374 LIHC patients and 50 normal individuals were acquired from the TCGA database. Through gene expression analyses, differentially expressed genes (DEGs) were determined. The 13 pyroptosis-related genes (PRGs) confirmed as potential prognostic factors through univariate Cox regression analysis were entered into Lasso and multivariate Cox regression to build a PRGs prognostic signature, containing four PRGs (BAK1, GSDME, NLRP6, and NOD2) determined as independent prognostic factors. mRNA levels were evaluated by qRT-PCR, while overall survival (OS) rates were assessed by the Kaplan–Meier method. Enrichment analyses were done to establish the mechanisms associated with differential survival status of LIHC patients from a tumor immunology perspective. Additionally, a risk score determined by the prognostic model could divide LIHC patients into low- and high-risk groups using median risk score as cut-off. A prognostic nomogram, derived from the prognostic model and integrating clinical characteristics of patients, was constructed. The prognostic function of the model was also validated using GEO, ICGC cohorts, and online databases Kaplan–Meier Plotter. Small interfering RNA-mediated knockdown of GSDME, as well as lentivirus-mediated GSDME knockdown, were performed to validate that knockdown of GSDME markedly suppressed growth of HCC cells both in vivo and in vitro. Collectively, our study demonstrated a PRGs prognostic signature that had great clinical value in prognosis assessment.

Abstract Reproductive-aged women experience somatic and affective symptoms of premenstrual syndrome (PMS) which affect their daily life and work, but there are limited tracking data on senior high school-age female students. To investigate the characteristics and incidence of PMS, and to detect the relationship between physical exercise participation and PMS in female students of senior high school. A prospective study was conducted among senior high school female students aged 14–16 years. The participant was asked to finish two questionnaires. One questionnaire collected information about demographic data and PMS symptoms which were recorded on a daily calendar named Daily Record of Severity of Problems (DRSP) per day. Another questionnaire was physical exercises participation questionnaire, collecting times of physical education (PE) class participation, exercise times, exercise type participation in morning exercise and recess exercise, times of morning exercise, times of recess exercise per week, recess exercise time, and autonomous exercise participation of students. The data were prospectively recorded for three consecutive months. Odds ratio (OR) with 95% confidence intervals (CI) was performed for results of the multivariate logistic regression analysis model. A total of 233 participants completed the prospective study, 78 (33.5%) participants experienced PMS. The incidence of the participants with mild PMS was 20.2% while moderate PMS was 11.6% and severe PMS was 1.7%. The most common somatic symptom was fatigue while the most common affective symptom was cannot concentrate. Participants with PE classes less than 2 times per week were 4.43 times (OR 4.43, 95% CI 1.18–16.6, p

Abstract Objectives The COVID-19 pandemic has caused an increase in psychiatric disorders in college students, particularly posttraumatic stress disorders (PTSD), depression, and anxiety. While existing studies assess the prevalence of these disorders and their predictors, they overlook potential complications caused by comorbidity between these disorders. To fill this gap, this study examined the prevalence of PTSD, depression, anxiety, and their comorbidity to inform targeted intervention for college students during the COVID-19 pandemic. Design Self-report questionnaires were used to assess 6,898 college students about six months after the COVID-19 outbreak. Results The results found that the prevalence of PTSD, depression, and anxiety were 15.5%, 32.2%, and 32.1% respectively, and the prevalence of comorbid PTSD and depression, comorbid PTSD and anxiety, comorbid depression and anxiety, and comorbid PTSD, depression, and anxiety symptoms were 11.5%, 11.6%, 20.4%, and 9.4% respectively. Moreover, left-behind status, lower economic status, previous trauma experiences, exposure to the pandemic, and rumination were risk factors of psychological distress, but self-disclosure was a protective factor for these disorders. Conclusion These results indicate that distinct psychiatric disorders may be comorbid in individuals, and are further influenced by pre-, within-, and post-disaster factors. Furthermore, psychological service targeted at college students should pay attention to comorbid symptoms rather than only symptoms of single disorders.

BackgroundFront-line power grid workers are required to face a variety of occupational hazards (such as aerial work), which make them susceptible to psychological problems and further reduce their performance efficiency and safety level. ObjectiveTo investigate the mental health status of front-line power grid workers and explore the influence of personality traits on mental health and the potential mediating role of work-family support between them. MethodsThis study was designed as a cross-sectional study. From January to June 2019, a cluster random sampling method was used to select two of the 20 power supply stations owned by a Guangdong power company. A total of 485 front-line power grid workers were included in the study. Sociodemographic characteristics were investigated, and NEO Five-Factor Inventory, Work-Family Support Scale, and Symptom Checklist 90 (SCL-90) were used in the survey. Spearman correlation analysis was conducted to analyze the correlations between measured variables. Structural equation modeling was used to analyze the relationships of personality traits, work-family support, and mental health, and Bootstrap analysis was used to test the mediating effect of work-family support on the relationship of personality traits and mental health. ResultsThe M (P25, P75) of total SCL-90 score was 134.00 (110.00, 167.00）, and 139 (28.66%) front-line power grid workers showed positive mental health symptoms. The correlation analysis indicated that among the front-line power grid workers, neuroticism score was negatively correlated with work-family support total score (rs=−0.356, P0.05), openness score was negatively correlated with the scores of all the other sub-dimensions of SCL-90 (rs=−0.091-−0.147, P0.05). The direct effect of agreeableness on work-family support was not statistically significant (P>0.05), while the direct effect on mental health symptoms was −0.180 (P

The association between post-traumatic growth (PTG) and cognitive empathy is well documented; however, few studies have tested the causal pathways explaining this association in school bullying victims’ later recovery and growth in the long term. This study used a longitudinal design to examine the reciprocal relations between cognitive empathy and post-traumatic growth (PTG) in school bullying victims. We screened 725 adolescents who had experienced school bullying as our final subjects out of the 2173 adolescents we surveyed over three periods (November 2019, 2020, and 2021). Controlling for gender, cross-lagged analysis revealed that both cognitive empathy at T1 and T2 predicted adolescents’ later PTG at T2 (γ = 0.096, p < 0.05) and T3 (γ = 0.085, p < 0.05), respectively, but the predictive effect across time points from PTG to cognitive empathy was not significant. The results delineated a specific directionality in the relation between cognitive empathy and PTG and suggested an important role of cognitive empathy in fostering school bullying victims’ later recovery and growth. These findings contribute to ongoing research into ways researchers and educators may help and support school bullying victims.

The commonly used POI route recommendation methods usually ignore the effects of tourists’ interests and transportation geographical conditions, and so may not output the optimal results. To solve the problems, we propose a POI route recommendation model based on symmetrical Naive Bayes classification spatial accessibility (NBCSA) and an improved cockroach swarm optimization algorithm (ICSOA), aiming to recommend POI routes that satisfy tourists’ interests and have the lowest travel costs under tourism transportation geographical conditions. Using the historical POIs visited by tourists as the training set, we construct an improved symmetrical Naive Bayes classification algorithm (NBCA), and the POIs in the destination city are divided into categories by tourists’ preferences. Then we propose an improved NBCSA model to calculate the spatial accessibility field strength (SAFS) for each category’s POIs. Based on the recommended POIs, we propose the ICSOA to recommend optimal POI routes. The experiment verifies that the proposed algorithm can effectively classify the POIs and recommend POIs that best match the tourists’ interests and produce the lowest travel costs. Compared with the TCA and GDA method, the proposed algorithm can output the POI routes with lower travel costs and has higher algorithm execution efficiency. Among the output optimal routes, the proposed algorithm can reduce costs by 5.62% at the lowest and 52.25% at the highest.

The diverse characteristics of heterogeneous data pose challenges in analyzing combined price and volume data. Therefore, appropriately handling heterogeneous financial data is crucial for accurate stock prediction. This article proposes a model that applies customized data processing methods tailored to the characteristics of different types of heterogeneous financial data, enabling finer granularity and improved feature extraction. By utilizing the structured multi-head attention mechanism, the model captures the impact of heterogeneous financial data on stock price trends by extracting data information from technical, financial, and sentiment indicators separately. Experimental results conducted on four representative individual stocks in China’s A-share market demonstrate the effectiveness of the proposed method. The model achieves an average MAPE of 1.378%, which is 0.429% lower than the benchmark algorithm. Moreover, the backtesting return rate exhibits an average increase of 28.56%. These results validate that the customized preprocessing method and structured multi-head attention mechanism can enhance prediction accuracy by attending to different types of heterogeneous data individually.