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Antimicrobial peptides are a type of small-molecule peptide that widely exist in nature and are components of the innate immunity of almost all living things. They play an important role in resisting foreign invading microorganisms. Antimicrobial peptides have a wide range of antibacterial activities against bacteria, fungi, viruses and other microorganisms. They are active against traditional antibiotic-resistant strains and do not easily induce the development of drug resistance. Therefore, they have become a hot spot of medical research and are expected to become a new substitute for fighting microbial infection and represent a new method for treating drug-resistant bacteria. This review briefly introduces the source and structural characteristics of antimicrobial peptides and describes those that have been used against common clinical microorganisms (bacteria, fungi, viruses, and especially coronaviruses), focusing on their antimicrobial mechanism of action and clinical application prospects.

Biofilms are communities of microorganisms that are attached to a biological or abiotic surface and are surrounded by a self-produced extracellular matrix. Cells within a biofilm have intrinsic characteristics that are different from those of planktonic cells. Biofilm resistance to antimicrobial agents has drawn increasing attention. It is well-known that medical device- and tissue-associated biofilms may be the leading cause for the failure of antibiotic treatments and can cause many chronic infections. The eradication of biofilms is very challenging. Many researchers are working to address biofilm-related infections, and some novel strategies have been developed and identified as being effective and promising. Nevertheless, more preclinical studies and well-designed multicenter clinical trials are critically needed to evaluate the prospects of these strategies. Here, we review information about the mechanisms underlying the drug resistance of biofilms and discuss recent progress in alternative therapies and promising strategies against microbial biofilms. We also summarize the strengths and weaknesses of these strategies in detail.

Fourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI, a fast image reconstruction framework based on deep learning (DL) is proposed. More specifically, a deep convolutional autoencoder network with symmetric skip connection architecture for real time 96 × 96 imaging at very low sampling rates (5–8%) is employed. The network is trained on a large image set and is able to reconstruct diverse images unseen during training. The promising experimental results show that the proposed FSPI coupled with DL (termed DL-FSPI) outperforms conventional FSPI in terms of image quality at very low sampling rates.

The potential of random pattern based computational ghost imaging (CGI) for real-time applications has been offset by its long image reconstruction time and inefficient reconstruction of complex diverse scenes. To overcome these problems, we propose a fast image reconstruction framework for CGI, called “DeepGhost”, using deep convolutional autoencoder network to achieve real-time imaging at very low sampling rates (10–20%). By transferring prior-knowledge from STL-10 dataset to physical-data driven network, the proposed framework can reconstruct complex unseen targets with high accuracy. The experimental results show that the proposed method outperforms existing deep learning and state-of-the-art compressed sensing methods used for ghost imaging under similar conditions. The proposed method employs deep architecture with fast computation, and tackles the shortcomings of existing schemes i.e., inappropriate architecture, training on limited data under controlled settings, and employing shallow network for fast computation.

YOLOv8-PEL shows outstanding performance in detection accuracy, computational efficiency, and generalization capability, making it suitable for real-time and resource-constrained applications. This study aims to address the challenges of vehicle detection in scenarios with fixed camera angles, where precision is often compromised for the sake of cost control and real-time performance, by leveraging the enhanced YOLOv8-PEL model. We have refined the YOLOv8n model by introducing the innovative C2F-PPA module within the feature fusion segment, bolstering the adaptability and integration of features across varying scales. Furthermore, we have proposed ELA-FPN, which further refines the model’s multi-scale feature fusion and generalization capabilities. The model also incorporates the Wise-IoUv3 loss function to mitigate the deleterious gradients caused by extreme examples in vehicle detection samples, resulting in more precise detection outcomes. We employed the COCO-Vehicle dataset and the VisDrone2019 dataset for our training, with the former being a subset of the COCO dataset that exclusively contains images and labels of cars, buses, and trucks. Experimental results demonstrate that the YOLOv8-PEL model achieved a mAP@0.5 of 66.9% on the COCO-Vehicle dataset, showcasing excellent efficiency with only 2.23 M parameters, 7.0 GFLOPs, a mere 4.5 MB model size, and 176.8 FPS—an increase from the original YOLOv8n’s inference speed of 165.7 FPS. Despite a marginal 0.2% decrease in accuracy compared to the original YOLOv8n, the parameters, GFLOPs, and model size were reduced by 25%, 13%, and 25%, respectively. The YOLOv8-PEL model excels in detection precision, computational efficiency, and generalizability, making it well-suited for real-time and resource-constrained application scenarios.

In recent years, due to the development and widespread utilization of metagenomic sequencing and metabolomics, the relationship between gut microbiota and human cardiovascular diseases (CVDs) has received extensive attention. A growing number of studies have shown a strong relationship between gut microbiota and CVDs, such as coronary atherosclerosis, hypertension (HTN) and heart failure (HF). It has also been revealed that intestinal flora-related metabolites, such as trimethylamine-N-oxide (TMAO), short-chain fatty acids (SCFA) and bile acids (BAs), are also related to the development, prevention, treatment and prognosis of CVDs. In this review, we presented and summarized the recent findings on the relationship between gut microbiota and CVDs, and concluded several currently known gut microbiota-related metabolites and the occurrence and development of CVDs.

Combination therapy of α-receptor blockers (α-RBs) and traditional Chinese medicine external therapy can serve as a treatment of chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS). α-RBs includes tamsulosin, terazosin and so on and the traditional Chinese medicine external therapy includes needling, moxibustion, acupoint catgut embedding, acupoint application, auricular point sticking and hot medicated compress and so forth. Currently, there is no study in which Bayesian network meta-analysis is applied to making a comparative analysis of efficacy of different combination therapies of α-RBs and traditional Chinese medicine external therapy in the treatment of CP/CPPS. Therefore, based on Bayesian algorithm, a network meta-analysis was conducted by us to make a comparison between different combination therapies of α-RBs and traditional Chinese medicine external therapy. A document retrieval was conducted in the databases PubMed, Cochrane Library, Embase, Web of science, China National Knowledge Infrastructure, WanFang Data Dissertations of China database, VIP China Science and Technology Journal Database, SinoMed. Literatures were searched for published in biomedical journals concerning clinical study on α-RBs combined with various traditional Chinese medicine external therapies in the treatment of CP/CPPS from inception of database to July 2022. Newest version risks of bias assessment tool (RoB2) was used to assess the risks of bias of studies included in this analysis. Stata 16.0 software and R4.1.3 software were used to make a Bayesian network meta-analysis and charts. 19 literatures were included involving 1739 patients concerning 12 interventions which were used in the treatment of CP/CPPS. With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. Concerning National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) total score, α-RBs+ moxibustion+ auricular point sticking was most likely to be optimal treatment, the therapy ranking second was α-RBs+ needling, and the therapy ranking third was α-RBs+ moxibustion. Pain score, voiding score and quality-of-life score are subdomains of the NIH-CPSI total score. With regard to pain score, α-RBs+ moxibustion was most likely to be optimal treatment. In reference to voiding and quality-of-life score, there was no statistically significant difference between the efficacy of various interventions. α-RBs+ needling, α-RBs+ moxibustion and α-RBs+ moxibustion+ auricular point sticking provided relatively good efficacy in the treatment of CP/CPPS. In these treatments, attention should be paid on α-RBs+ needling and α-RBs+ moxibustion which ranked higher many times in the evaluation of various outcome indicators. However, there still were certain limitations in this study, so large-sample clinical randomized control trials with a rigor design following the evidence-based medicine standards need to be conducted to justify the results of this study. [https://www.crd.york.ac.uk/prospero/], identifier: [CRD42022341824].

Objective To delineate the clinical differences between Pneumocystis jirovecii pneumonia (PJP) and colonization, identify independent risk factors associated with PJP development, and construct a multidimensional diagnostic model to address the ongoing clinical challenge of accurately distinguishing P. jirovecii infection status in practice. Materials and methods This retrospective study analyzed the clinical characteristics, imaging findings, and laboratory parameters of patients who tested positive for P. jirovecii by next-generation sequencing (NGS) at the First Hospital of Jilin University between January 2014 and October 2024. Multivariable logistic regression was performed to determine independent predictors of PJP. Results Of the 292 patients included in the analysis (210 diagnosed with PJP and 82 classified as colonized), those with PJP had significantly higher rates of immunosuppression (64.4% vs. 9.9%, P <  0.001) and markedly increased P. jirovecii sequence counts from NGS (median: 1,686 vs. 4, P <  0.001).Human immunodeficiency virus coinfection, decreased lymphocyte count, elevated BDG levels, and increased LDH levels were identified as independent risk factors for PJP. A diagnostic model incorporating these four variables demonstrated excellent predictive capability, yielding an area under the receiver operating characteristic curve of 0.892 ( P <  0.001; 95% confidence interval: 0.855–0.929). The optimal NGS sequence count threshold for differentiating PJP from colonization was determined to be 37, achieving a sensitivity of 91% and a specificity of 87.8% (area under the receiver operating characteristic curve: 0.964). Conclusions The developed risk prediction model—comprising lymphocyte count, BDG, and LDH levels—facilitates rapid, pre-NGS clinical risk stratification for PJP, enabling prompt and informed therapeutic decision-making. When NGS results yield a P. jirovecii -specific sequence reads below the cutoff value of 37, a definitive diagnosis of PJP is unlikely. However, such findings should be interpreted in the context of the patient’s clinical presentation and assessed using the diagnostic model to ensure an accurate evaluation of infection status.

Sarcopenia has become a heavy disease burden among the elderly. Lipid metabolism was reported to be involved in many degenerative diseases. This study aims to investigate the association between dysregulated lipid metabolism and sarcopenia in geriatric inpatients. This cross-sectional study included 303 patients aged ≥ 60, of which 151 were diagnosed with sarcopenia. The level of total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), homocysteine (HCY), BMI, and fat percentage, were compared between sarcopenia and non-sarcopenia patients. The Spearman correlation coefficient was used to estimate the association between sarcopenia and the level of lipid metabolism. To determine risk factors related to sarcopenia, a multivariate logistic regression analysis was carried out. Risk prediction models were constructed based on all possible data through principal component analysis (PCA), Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGboost). We observed rising prevalence of sarcopenia with increasing age, decreasing BMI, and fat percentage ( p  < 0.001, Cochran Armitage test). Multivariate logistic regression analysis revealed sarcopenia’s risk factors, including older age, male sex, lower levels of BMI, TC, and TG, and higher levels of LDL and HCY ( p  < 0.05). The sarcopenia risk prediction model showed the risk prediction value of sarcopenia, with the highest area under the receiver operating curve (AUC) of 0.775. Our study provided thorough insight into the risk factors associated with sarcopenia. It demonstrated that an increase in lipid metabolism-related parameters (BMI, TG, TC), within normal reference ranges, may be protective against sarcopenia. The present study can illuminate the direction and significance of lipid metabolism-related factors in preventing sarcopenia.

Optimal pH conditions for efficient artificial photosynthesis, hydrogen/oxygen evolution reactions, and photoreduction of carbon dioxide are now successfully achievable with catalytic bipolar membranes-integrated water dissociation and in-situ acid-base generations. However, inefficiency and instability are severe issues in state-of-the-art membranes, which need to urgently resolve with systematic membrane designs and innovative, inexpensive junctional catalysts. Here we show a shielding and in-situ formation strategy of fully-interconnected earth-abundant goethite Fe +3 O(OH) catalyst, which lowers the activation energy barrier from 5.15 to 1.06 eV per HO − H bond and fabricates energy-efficient, cost-effective, and durable shielded catalytic bipolar membranes. Small water dissociation voltages at limiting current density (U LCD : 0.8 V) and 100 mA cm −2 (U 100 : 1.1 V), outstanding cyclic stability at 637 mA cm −2 , long-time electro-stability, and fast acid-base generations (H 2 SO 4 : 3.9 ± 0.19 and NaOH: 4.4 ± 0.21 M m −2 min −1 at 100 mA cm −2 ) infer confident potential use of the novel bipolar membranes in emerging sustainable technologies. Bipolar membranes integrated water dissociation and acid-base generations have great potential in emerging sustainable technologies but remains inefficient. Here, the authors circumvent this inefficiency and instability of the membranes by developing polyaniline shielded catalytic bipolar membranes.