Alteration of the gut microbiome in patients with heart failure: A systematic review and meta-analysis.

Recent research has revealed that alterations of the gut microbiome (GM) play a comprehensive role in the pathophysiology of HF. However, findings in this field remain controversial. In this study, we focus on differences in GM diversity and abundance between HF patients and non-HF people, based on previous 16 S ribosomal RNA (16rRNA) gene sequencing. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search of PubMed, Web of Science, Embase, Cochrane Library, and Ovid databases using the keyword "Heart failure" and "Gastrointestinal Microbiome". A significant decrease in alpha diversity was observed in the HF patients (Chao1, I2 = 87.5 %, p < 0.001; Shannon index, I2 = 62.8 %, p = 0.021). At the phylum level, the HF group exhibited higher abundances of Proteobacteria (I2 = 92.0 %, p = 0.004) and Actinobacteria (I2 = 82.5 %, p = 0.010), while Bacteroidetes (I2 = 45.1 %, p = 0.017) and F/B ratio (I2 = 0.0 %, p＜0.001) were lower. The Firmicutes showed a decreasing trend but did not reach statistical significance (I2 = 82.3 %, p = 0.127). At the genus level, the relative abundances of Streptococcus, Bacteroides, Alistipes, Bifidobacterium, Escherichia-Shigella, Enterococcus and Klebsiella were increased in the HF group, whereas Ruminococcus, Faecalibacterium, Dorea and Megamona exhibited decreased relative abundances. Dialister, Blautia and Prevotella showed decreasing trends but without statistical significance. This observational meta-analysis suggests that GM changes are associated with HF, manifesting as alterations in GM abundance, disruptions in the production of short-chain fatty acids (SCFAs) bacteria, and an increase in trimethylamine N-oxide (TMAO) producing bacteria.

Mycoplasma pneumoniae infection outbreak in Guangzhou, China after COVID-19 pandemic.

BACKGROUNDS: Mycoplasma pneumoniae (M. pneumoniae) is a common pathogen causing respiratory diseases in children. This study aimed to characterize epidemiological and disease severity shifts of M. pneumoniae: infections in Guangzhou, China during and after the coronavirus disease 2019 (COVID-19) pandemic. METHODS: Throat swab samples were obtained from 5405 hospitalized patients with symptoms of acute respiratory infections to detect M. pneumoniae. Differences in epidemiological and clinical characteristics of M. pneumoniae: infections were investigated during 2020-2022 and after COVID-19 pandemic (2023). RESULTS: M. pneumoniae were detected in 849 (15.6%, 849/5405) patients. The highest annual positive rate was 29.4% (754/2570) in 2023, followed by 5.3% (72/1367) in 2022, 1.2% (12/1015) in 2021, and 2.0% (11/553) in 2020, with significantly increasing annual prevalence from 2020 to 2023. M. pneumoniae incidence peaked between July and December post-COVID-19 pandemic in 2023, with the highest monthly positive rate (56.4%, 165/293). Clinical characteristics and outcomes of patients with M. pneumoniae did not vary between periods during and after COVID-19 pandemic except that patients with M. pneumoniae post-COVID-19 pandemic were more likely to develop fever. Patients with severe M. pneumoniae pneumonia (SMPP) were more likely to develop respiratory complications, myocardial damage, and gastrointestinal dysfunction than those with non-SMPP. Patients with SMPP had lower lymphocytes, CD3+ T cells, CD4+ T cells, CD8+ T cells, B cells, and higher IL-4, IL-6, IL-10 levels than those with non-SMPP. Bronchoalveolar lavage fluid specimens from infected patients were obtained to identify macrolide resistance mutations. Macrolide-resistant M. pneumoniae (MRMP) proportion in 2023 was 91.1% (215/236). CONCLUSION: Outbreaks of M. pneumoniae: occurred in Guangzhou, China in 2023 upon Non-pharmaceutical interventions easing. Despite the increasing incidence of M. pneumoniae, the disease severity remained similar during and after the COVID-19 pandemic.

Super-resolution reconstruction for early cervical cancer magnetic resonance imaging based on deep learning.

This study aims to develop a super-resolution (SR) algorithm tailored specifically for enhancing the image quality and resolution of early cervical cancer (CC) magnetic resonance imaging (MRI) images. The proposed method is subjected to both qualitative and quantitative analyses, thoroughly investigating its performance across various upscaling factors and assessing its impact on medical image segmentation tasks. The innovative SR algorithm employed for reconstructing early CC MRI images integrates complex architectures and deep convolutional kernels. Training is conducted on matched pairs of input images through a multi-input model. The research findings highlight the significant advantages of the proposed SR method on two distinct datasets at different upscaling factors. Specifically, at a 2× upscaling factor, the sagittal test set outperforms the state-of-the-art methods in the PSNR index evaluation, second only to the hybrid attention transformer, while the axial test set outperforms the state-of-the-art methods in both PSNR and SSIM index evaluation. At a 4× upscaling factor, both the sagittal test set and the axial test set achieve the best results in the evaluation of PNSR and SSIM indicators. This method not only effectively enhances image quality, but also exhibits superior performance in medical segmentation tasks, thereby providing a more reliable foundation for clinical diagnosis and image analysis.

Analysis of the epidemiological situation of influenza in Guangzhou under the prevention and control of COVID-19 in June 2022.

OBJECTIVE: Under the prevention and control measures of COVID-19, the epidemiological situation of respiratory pathogens is not well known. Understanding the patterns of respiratory pathogens epidemiology under the prevention and control measures of COVID-19 is important to guide resource allocation for existing and future treatment and prevention strategies. METHODS: In total, 659 fever outpatients nasopharyngeal swabs were collected at fever illness onset during June in 2022 at the First Hospital of Guangzhou Medical University. Swabs were tested by real-time fluorescent single-tube multiplex polymerase chain reaction (PCR) for 12 respiratory pathogens. Moreover, 108 of the 659 swabs were tested for influenza virus antigen. RESULTS: At least one pathogen was detected in 477 (72.38%) of 659 fever outpatients with multiple pathogens identified in 25 (3.79%). The highest multiple infectious pattern is parainfluenza virus in combination with influenza (five cases). Influenza A virus (IFA), human rhinovirus (HRV), and parainfluenza virus are the three leading virus pathogens with proportions of 64.64%, 5.01%, and 2.88%. School-age children and adult groups have the highest pathogens positivity rate of 81.28% and 83.87%. CONCLUSION: A high proportion of adolescents and adults has respiratory pathogens detected during fever illnesses during June in 2022 under the prevention and control of COVID-19. These data indicate that diagnosis, prevention, and control of respiratory tract infection should be paid attention under the prevention and control of COVID-19.

A deep learning-based automatic image quality assessment method for respiratory phase on computed tomography chest images.

Background: Computed tomography (CT) chest scans have become commonly used in clinical diagnosis. Image quality assessment (IQA) for CT images plays an important role in CT examination. It is worth noting that IQA is still a manual and subjective process, and even experienced radiologists make mistakes due to human limitations (fatigue, perceptual biases, and cognitive biases). There are also kinds of biases because of poor consensus among radiologists. Excellent IQA methods can reliably give an objective evaluation result and also reduce the workload of radiologists. This study proposes a deep learning (DL)-based automatic IQA method, to assess whether the image quality of respiratory phase on CT chest images are optimal or not, so that the CT chest images can be used in the patient's physical condition assessment. Methods: This retrospective study analysed 212 patients' chest CT images, with 188 patients allocated to a training set (150 patients), validation set (18 patients), and a test set (20 patients). The remaining 24 patients were used for the observer study. Data augmentation methods were applied to address the problem of insufficient data. The DL-based IQA method combines image selection, tracheal carina segmentation, and bronchial beam detection. To automatically select the CT image containing the tracheal carina, an image selection model was employed. Afterward, the area-based approach and score-based approach were proposed and used to further optimize the tracheal carina segmentation and bronchial beam detection results, respectively. Finally, the score about the image quality of the patient's respiratory phase images given by the DL-based automatic IQA method was compared with the mean opinion score (MOS) given in the observer study, in which four blinded experienced radiologists took part. Results: The DL-based automatic IQA method achieved good performance in assessing the image quality of the respiratory phase images. For the CT sequence of the same patient, the DL-based IQA method had an accuracy of 92% in the assessment score, while the radiologists had an accuracy of 88%. The Kappa value of the assessment score between the DL-based IQA method and radiologists was 0.75, with a sensitivity of 85%, specificity of 91%, positive predictive value (PPV) of 92%, negative predictive value (NPV) of 93%, and accuracy of 88%. Conclusions: This study develops and validates a DL-based automatic IQA method for the respiratory phase on CT chest images. The performance of this method surpassed that of the experienced radiologists on the independent test set used in this study. In clinical practice, it is possible to reduce the workload of radiologists and minimize errors caused by human limitations.

Injectable and Conductive Nanomicelle Hydrogel with α-Tocopherol Encapsulation for Enhanced Myocardial Infarction Repair.

Substantial advancements have been achieved in the realm of cardiac tissue repair utilizing functional hydrogel materials. Additionally, drug-loaded hydrogels have emerged as a research hotspot for modulating adverse microenvironments and preventing left ventricular remodeling after myocardial infarction (MI), thereby fostering improved reparative outcomes. In this study, diacrylated Pluronic F127 micelles were used as macro-cross-linkers for the hydrogel, and the hydrophobic drug α-tocopherol (α-TOH) was loaded. Through the in situ synthesis of polydopamine (PDA) and the incorporation of conductive components, an injectable and highly compliant antioxidant/conductive composite FPDA hydrogel was constructed. The hydrogel exhibited exceptional stretchability, high toughness, good conductivity, cell affinity, and tissue adhesion. In a rabbit model, the material was surgically implanted onto the myocardial tissue, subsequent to the ligation of the left anterior descending coronary artery. Four weeks postimplantation, there was discernible functional recovery, manifesting as augmented fractional shortening and ejection fraction, alongside reduced infarcted areas. The findings of this investigation underscore the substantial utility of FPDA hydrogels given their proactive capacity to modulate the post-MI infarct microenvironment and thereby enhance the therapeutic outcomes of myocardial infarction.