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Introduction: Although programmed death ligand 1 (PD-L1) inhibitor plus chemotherapy regimen is a promising strategy for malignant tumors, it can induce significant immune-related adverse events, such as immune-related pneumonitis. Here, we report the first case of lethal immune-related pneumonitis in an Asian patient receiving anti-PD-L1 treatment. Case Presentation: A 68-year-old man was diagnosed with small cell lung cancer and interstitial pneumonia. After his pulmonary infection was relieved by comprehensive treatment, the patient received first-line treatment with durvalumab plus etoposide and carboplatin. Two weeks after starting durvalumab treatment, the patient had chest pain and shortness of breath. He was diagnosed with immune-induced pneumonia and treated with methylprednisolone, cefoperazone, and sulbactam, followed by oxygen and pirfenidone. Oxygen partial pressure decreased to 58 mm Hg within next the 4 days and laboratory assessment suggested cytokine storm. The patient underwent 2 plasma exchanges, one double filtration plasmapheresis and oxygen saturation decreased continuously. The patient died 1 month after durvalumab treatment. Conclusion: Immune-related pneumonitis induced by PD-L1 inhibitors is rare but life-threatening. Infection should be ruled out before starting immunotherapy.

Abstract Improving access to HIV/AIDS healthcare services is of great concern to government and policymakers striving to strengthen overall public health. How to reasonably allocate HIV/AIDS healthcare resources and maximize the equality of access to healthcare services across subdistrict areas has become an urgent problem to be solved. However, there is limited research on this topic in China. It is necessary to evaluate spatial accessibility to improve the accessibility and equity of HIV/AIDS healthcare services. In this study, the improved multi-modal two-step floating catchment area (2SFCA) and inverted 2SFCA (i2SFCA) methods are used to measure the spatial accessibility of HIV/AIDS healthcare services and the crowdedness of the healthcare sites in Shandong Province, China. Then, the theoretical supply and the optimal spatial distribution of resources are calculated and visualized by minimizing the accessibility gaps between demand locations. This study showed that the spatial accessibility of HIV/AIDS service resources in Shandong Province was concentrated and unevenly distributed, and the accessibility scores in the marginal areas of prefecture-level cities were significantly lower than those in other areas. Regions with a large number of doctors had significantly higher levels of spatial accessibility. The ART accessibility scores in the southwest of Shandong Province were higher than those in other regions. As the travel friction coefficient increased, the accessibility scores formed an approximately circular cluster distribution centered on the healthcare sites in geographical distribution. More ART drugs needed to be supplied in marginal areas and more doctors were needed to work on HIV/AIDS in urban areas to address the spatial distribution imbalance of HIV/AIDS healthcare services. This study profoundly analyzed the spatial accessibility of HIV/AIDS healthcare services and provided essential references for decision-makers. In addition, it gives a significant exploration for achieving the goal of equal access to HIV/AIDS healthcare services in the future.

Many biological studies show that microRNAs (miRNAs) play an indispensable role in the regulation of various biological processes. MiRNAs are significant biomakers in disease diagnosis, aiding in the understanding of pathogenesis and facilitating the identification, diagnosis, and treatment of various diseases. However, the exact mechanism by which miRNAs influence the development of these diseases remains incompletely understood. Thus, it is crucial to develop a computational method to identify unknown miRNA-disease associations. In this study, we designed a computational framework based on singular value decomposition (SVD) and node2vec to predict unknown miRNA-disease associations (SNMDA). We use SVD technique to extract the linear features of miRNAs and diseases. The node2vec method is applied to learn the non-linear embeddings of miRNAs and diseases. We combine the linear feature and non-linear feature to get a new feature vector and feed it into the Gradient Boosting (GB) classifier for binary classification prediction. According to the experimental findings, SNMDA demonstrated an average area under the curve (AUC) of 0.9608 during five-fold cross-validation. Compared with the other Cutting-edge methods, SNMDA achieved the highest AUC value. Furthermore, the case studies on gastric cancer, malignant esophageal lesions, and lung tumors validate the effectiveness of SNMDA. The comprehensive experimental results demonstrate that SNMDA is effective in identifying unknown miRNA-disease associations.

Arbuscular mycorrhizal fungi (AMF) play a key role in regulating the carbon cycle in terrestrial ecosystems. However, there is little information on how AMF inoculation affects the carbon fluxes of paddy fields, which are major sources of global carbon emissions. We, therefore, designed an experiment to study the effects of AMF inoculation on methane and carbon dioxide emissions from a paddy field. Results showed that: (1) Among the tested factors, the C/N ratio was the main environmental determinant of microbial community structure in the investigated soil; (2) compared with traditional fertilisation (control), the soil C/N ratio increased by 2.1~15.2% and 1.4~10.5% as a result of AMF application alone (M) or in combination with mineral fertiliser (FM) throughout the growing season, respectively. This change shifted microbial community composition to higher G+/G- bacterial and fungal/bacterial ratios; (3) the microbial community change favoured soil carbon retention. Methane (CH4) emission peaks were reduced by 59.4% and 76.0% versus control in the M treatment and by 52.5% and 29.4% in the FM treatment in the midseason and end-of-season drainage periods, and CO2 emission peaks were reduced by 70.1% and 52.3% in the M plots and by 55.4% and 66.4% in the FM plots.

BackgroundMaternal immune activation (MIA) is a mature means to construct a schizophrenia model. However, some preclinical studies have reported that a MIA-induced schizophrenia model seemed to have gender heterogeneity in behavioral phenotype. On the other hand, the MIA’s paradigms were diverse in different studies, and many details could affect the effect of MIA. To some extent, it is not credible and scientific to directly compare the gender differences of different MIA programs. Therefore, it is necessary to study whether the sex of the exposed offspring leads to behavioral differences on the premise of maintaining a consistent MIA mode.MethodsAn animal model of schizophrenia was established by the administration of 10 mg/kg Poly (I: C) when dams were on day 9 of gestation. Then, a number of female and male offspring completed a series of behavioral tests during postnatal days 61–75.ResultsCompared with the female control group (n = 14), female MIA offspring (n = 12) showed a longer movement distance (d = 1.07, p < 0.05) and higher average speed (d = 1.08, p < 0.05) in the open field test (OFT). In the Y maze test, the percentage of entering the novel arm of female MIA offspring was lower (d = 0.92, p < 0.05). Compared with the male control group (n = 14), male MIA offspring (n = 13) displayed less movement distance (d = 0.93, p < 0.05) and a lower average speed (d = 0.94, p < 0.05) in the OFT. In the Y maze test, the proportion of exploration time in the novel arm of male MIA offspring was lower (d = 0.96, p < 0.05). In the EPM, male MIA offspring showed less time (d = 0.85, p < 0.05) and a lower percentage of time spent in the open arms (d = 0.85, p < 0.05). Male MIA offspring also had a lower PPI index (76 dB + 120 dB, d = 0.81, p < 0.05; 80 dB + 120 dB, d = 1.45, p < 0.01).ConclusionsOur results showed that the behavioral phenotypes induced by prenatal immune activation were highly dependent on the sex of the offspring.

The occurrence of hand, foot, and mouth disease (HFMD) is closely related to meteorological factors. However, location-specific characteristics, such as persistent air pollution, may increase the complexity of the impact of meteorological factors on HFMD, and studies across different areas and populations are largely lacking. In this study, a two-stage multisite time-series analysis was conducted using data from 16 cities in Shandong Province from 2015 to 2019. In the first stage, we obtained the cumulative exposure–response curves of meteorological factors and the number of HFMD cases for each city. In the second stage, we merged the estimations from the first stage and included city-specific air pollution variables to identify significant effect modifiers and how they modified the short-term relationship between HFMD and meteorological factors. High concentrations of air pollutants may reduce the risk effects of high average temperature on HFMD and lead to a distinct peak in the cumulative exposure-response curve, while lower concentrations may increase the risk effects of high relative humidity. Furthermore, the effects of average wind speed on HFMD were different at different levels of air pollution. The differences in modification effects between subgroups were mainly manifested in the diversity and quantity of significant modifiers. The modification effects of long-term air pollution levels on the relationship between sunshine hours and HFMD may vary significantly depending on geographical location. The people in age＜3 and male groups were more susceptible to long-term air pollution. These findings contribute to a deepening understanding of the relationship between meteorological factors and HFMD and provide evidence for relevant public health decision-making.

Infrared ship detection is of great significance due to its broad applicability in maritime surveillance, traffic safety and security. Multiple infrared sensors with different spectral sensitivity provide enhanced sensing capabilities, facilitating ship detection in complex environments. Nevertheless, current researches lack discussion and exploration of infrared imagers in different spectral ranges for marine objects detection. Furthermore, for unmanned marine vehicles (UMVs), e.g., unmanned surface vehicles (USVs) and unmanned ship (USs), detection and perception are usually performed in embedded devices with limited memory and computation resource, which makes traditional convolutional neural network (CNN)-based detection methods struggle to leverage their advantages. Aimed at the task of sea surface object detection on USVs, this paper provides lightweight CNNs with high inference speed that can be deployed on embedded devices. It also discusses the advantages and disadvantages of using different sensors in marine object detection, providing a reference for the perception and decision-making modules of USVs. The proposed method can detect ships in short-wave infrared (SWIR), long-wave infrared (LWIR) and fused images with high-performance and high-inference speed on an embedded device. Specifically, the backbone is built from bottleneck depth-separable convolution with residuals. Generating redundant feature maps by using cheap linear operation in neck and head networks. The learning and representation capacities of the network are promoted by introducing the channel and spatial attention, redesigning the sizes of anchor boxes. Comparative experiments are conducted on the infrared ship dataset that we have released which contains SWIR, LWIR and the fused images. The results indicate that the proposed method can achieve high accuracy but with fewer parameters, and the inference speed is nearly 60 frames per second (FPS) on an embedded device.

In tumor treatment, the highly disordered vascular system and lack of accumulation of chemotherapeutic drugs in tumors severely limit the therapeutic role of nanocarriers. Smaller drug-containing nanoparticles (NPs) can better penetrate the tumor but are easily removed, which severely limits the tumor-killing properties of the drug. The chemotherapeutic medication doxorubicin (DOX) is highly toxic to the heart, but this toxicity can be effectively mitigated and the combined anticancer effect can be enhanced by clinically incorporating curcumin (CUR) as part of the dual therapy. We designed a small-molecule peptide, Pep1, containing a targeting peptide (CREKA) and a pH-responsive moiety. These NPs can target the blood vessels in tumor microthrombi and undergo a morphological shift in the tumor microenvironment. This process enhances the penetration and accumulation of drugs, ultimately improving the effectiveness of cancer treatment. In vitro and in vivo experiments demonstrated that this morphological transformation allowed rapid and effective drug release into tumors, the effective inhibition of tumor angiogenesis, and the promotion of tumor cell apoptosis, thus effectively killing tumor cells. Our findings provide a novel and simple approach to nhibit the growth and metastasis of hepatocellular carcinoma.

With the rapid advancement of wireless communication technology, automatic modulation classification (AMC) plays a crucial role in drone communication systems, ensuring reliable and efficient communication in various non-cooperative environments. Deep learning technology has demonstrated significant advantages in the field of AMC, effectively and accurately extracting and classifying modulation signal features. However, existing deep learning models often have high computational costs, making them difficult to deploy on resource-constrained drone communication devices. To address this issue, this study proposes a lightweight Mobile Automatic Modulation Classification Transformer (MobileAmcT). This model combines the advantages of lightweight convolutional neural networks and efficient Transformer modules, incorporating the Token and Channel Conv (TCC) module and the EfficientShuffleFormer module to enhance the accuracy and efficiency of the automatic modulation classification task. The TCC module, based on the MetaFormer architecture, integrates lightweight convolution and channel attention mechanisms, significantly improving local feature extraction efficiency. Additionally, the proposed EfficientShuffleFormer innovatively improves the traditional Transformer architecture by adopting Efficient Additive Attention and a novel ShuffleConvMLP feedforward network, effectively enhancing the global feature representation and fusion capabilities of the model. Experimental results on the RadioML2016.10a dataset show that compared to MobileNet-V2 (CNN-based) and MobileViT-XS (ViT-based), MobileAmcT reduces the parameter count by 74% and 65%, respectively, and improves classification accuracy by 1.7% and 1.09% under different SNR conditions, achieving an accuracy of 62.93%. This indicates that MobileAmcT can maintain high classification accuracy while significantly reducing the parameter count and computational complexity, clearly outperforming existing state-of-the-art AMC methods and other lightweight deep learning models.

Background. Diabetes self-management education (DSME) provides diabetic patients with knowledge of diabetes, requires attention and recording of dietary habits, and increases the frequency and accuracy of blood glucose monitoring. DSME also achieves better blood glucose control, thus benefiting diabetic patients and reducing the risk of diabetes complications. However, few studies have systematically examined whether DSME follows AADE 7 Self-Care Behaviors (AADE7™). Therefore, this study aimed to investigate the control effect of AADE7™-based management on laboratory test indicators of diabetic patients. Methods. The patients with diabetes who received shared care management in our hospital between June 2014 and April 2022 were analyzed retrospectively. According to the process of outpatient consultation, each patient received health education provided by diabetes education nurses and dietitians after consultation. Health education was a process from assessment to health guidance. The basic information of all patients was recorded, and AADE7™ behavior assessment and health education session were conducted through interviews. A total of 13,650 were given shared care management, requiring more than 6 follow-up visits per year, as well as health education. It was reassessed annually according to AADE standards. The impact of the patients’ behavior change after the AADE7™-based management on the relevant test indicators was observed. Results. After eight years of intervention, a total of 8319 samples were obtained after excluding the outliers. Stepwise regression analysis was performed, and the results showed that, with other conditions held constant, a greater number of days per week to follow a healthy diet, to take hypoglycemic medication as prescribed, to monitor blood glucose, and to exercise and higher education level were associated with lower levels of glycosylated hemoglobin. The change from drinking to nondrinking was associated with lower triglycerides. If low blood glucose was monitored, patients who reviewed and took immediate action showed lower levels of low-density lipoprotein, urine microalbumin, and urine microalbumin/creatinine ratio compared with those without review and immediate action. Significance tests for each term showed P value

In this study, we studied the effect of the addition of citrus fibers on the yield and quality of sauced beef. Sauced beef added with different types of citrus fibers (CF100L80, CF400L80 and CF100H60) by tumbling at different amounts (0.5%, 1.0% and 1.5%) was prepared and evaluated for yield, moisture distribution, pH, residual citrus fiber, color, texture, distribution of citrus fiber and sensory evaluation. The results demonstrated that the incorporation of citrus fibers significantly enhanced the yield of sauced beef, with the highest values of 78.73% and 78.35% being observed in sauced beef supplemented with 1.5% CF400L80 and 0.5% CF100H60, respectively. Furthermore, it was found that the addition of citrus fibers improved the water retention capacity of sauced beef not through pH changes but rather through water adsorption of residual citrus fiber in the meat. Among the prepared samples, sauced beef with the addition of 1.5% CF400L80 and 1.0% CF100L80 had the highest residual rate of citrus fiber of 68.57% and 66.31%, respectively. The scanning electron micrograph (SEM) results showed that citrus fibers accumulated on the surface of the muscle bundles and increased the yield of sauced beef through water adsorption and swelling. Also, the addition of citrus fibers decreased the hardness, stickiness and chewiness of sauced beef (P < 0.05). The sensory evaluation results demonstrated that the incorporation of citrus fibers significantly enhanced the texture and taste attributes of sauced beef, with the highest sensory evaluation score being observed for the samples with the addition of 0.5% CF100H60. In conclusion, the addition of 0.5% CF100H60 citrus fibers was the most effective in enhancing both the yield and sensory quality parameters of sauced beef.