Search

Abstract. Background:. Accurately and efficiently extracting microbial genomic sequences from complex metagenomic data is crucial for advancing our understanding in fields such as clinical diagnostics, environmental microbiology, and biodiversity. As sequencing technologies evolve, this task becomes increasingly challenging due to the intricate nature of microbial communities and the vast amount of data generated. Especially in intensive care units (ICUs), infections caused by antibiotic-resistant bacteria are increasingly prevalent among critically ill patients, significantly impacting the effectiveness of treatments and patient prognoses. Therefore, obtaining timely and accurate information about infectious pathogens is of paramount importance for the treatment of patients with severe infections, which enables precisely targeted anti-infection therapies, and a tool that can extract microbial genomic sequences from metagenomic dataset would be of help. Methods:. We developed MetaGeneMiner to help with retrieving specific microbial genomic sequences from metagenomes using a k-mer-based approach. It facilitates the rapid and accurate identification and analysis of pathogens. The tool is designed to be user-friendly and efficient on standard personal computers, allowing its use across a wide variety of settings. We validated MetaGeneMiner using eight metagenomic samples from ICU patients, which demonstrated its efficiency and accuracy. Results:. The software extensively retrieved coding sequences of pathogens Acinetobacter baumannii and herpes simplex virus type 1 and detected a variety of resistance genes. All documentation and source codes for MetaGeneMiner are freely available at https://gitee.com/sculab/MetaGeneMiner. Conclusions:. It is foreseeable that MetaGeneMiner possesses the potential for applications across multiple domains, including clinical diagnostics, environmental microbiology, gut microbiome research, as well as biodiversity and conservation biology. Particularly in ICU settings, MetaGeneMiner introduces a novel, rapid, and precise method for diagnosing and treating infections in critically ill patients. This tool is capable of efficiently identifying infectious pathogens, guiding personalized and precise treatment strategies, and monitoring the development of antibiotic resistance, significantly impacting the diagnosis and treatment of severe infections.

Abstract Background Postoperative pulmonary complication (PPC) is a leading cause of mortality and poor outcomes in postoperative patients. No studies have enrolled intensive care unit (ICU) patients after noncardiac thoracic surgery, and effective prediction models for PPC have not been developed. This study aimed to explore the incidence and risk factors and construct prediction models for PPC in these patients. Methods This study retrospectively recruited patients admitted to the ICU after noncardiac thoracic surgery at West China Hospital, Sichuan University, from July 2019 to December 2022. The patients were randomly divided into a development cohort and a validation cohort at a 70% versus 30% ratio. The preoperative, intraoperative and postoperative variables during the ICU stay were compared. Univariate and multivariate logistic regression analyses were applied to identify candidate predictors, establish prediction models, and compare the accuracy of the models with that of reported risk models. Results A total of 475 ICU patients were enrolled after noncardiac thoracic surgery (median age, 58; 72% male). At least one PPC occurred in 171 patients (36.0%), and the most common PPC was pneumonia (153/475, 32.21%). PPC significantly increased the duration of mechanical ventilation (p

Abstract Background Airway pressure release ventilation (APRV) has become increasingly popular for the management of acute respiratory distress syndrome (ARDS); however, its clinical impact remains a topic of debate. Furthermore, there is a gap between the guidelines and the actual clinical practices in mechanical ventilation management for ARDS. This survey aimed to explore the utilization of APRV and mechanical ventilation strategies for ARDS in Chinese intensive care unit (ICU) clinicians. Methods A comprehensive 34‐item survey was distributed online platforms amongst ICU clinicians across mainland China from June to August 2019. Results A total of 420 valid responses were collected, with 57.4% (241) originating from academic hospitals and 42.6% (179) from non‐academic hospitals. Of the respondents, 98.6% (414) recognized the significance of low tidal volume ventilation for ARDS prognosis, 85.2% adhered to a tidal volume below 8 mL/kg predicted body weight, and most (46.4%) selected the initial positive end‐expiratory pressure within the range of 5–10 cmH2O based on experience. Among the respondents, 62.1% (261) reported familiarity with APRV and 41.9% (176) had implemented APRV. Of those who had utilized APRV, 93.2% (164) believed in its effectiveness for ARDS patients and 69.3% (122) advocated for early application of APRV. Substantial variations were noted regarding APRV initiation settings and the preservation of spontaneous breathing during APRV. Academic hospitals exhibited higher usage rates of lung recruitment, neuromuscular blockade, prone ventilation, and acquaintance with and utilization of APRV compared to non‐academic hospitals (all p values ≤ 0.001). Conclusions Our findings highlight opportunities for improvement in mechanical ventilation management for ARDS, particularly in non‐academic hospitals. Additionally, a significant proportion of clinicians demonstrated limited knowledge of APRV, and there was a lack of consensus on its application. Further training and larger‐scale clinical trials are required to validate the efficacy and utilization of APRV in managing ARDS.

Abstract Intervertebral Disc Herniation (IVDH) is a common spinal disease in dogs, significantly impacting their health, mobility, and overall well-being. This study initiates an effort to automate the detection and localization of IVDH lesions in veterinary MRI scans, utilizing advanced artificial intelligence (AI) methods. A comprehensive canine IVDH dataset, comprising T2-weighted sagittal MRI images from 213 pet dogs of various breeds, ages, and sizes, was compiled and utilized to train and test the IVDH detection models. The experimental results showed that traditional two-stage detection models reliably outperformed one-stage models, including the recent You Only Look Once X (YOLOX) detector. In terms of methodology, this study introduced a novel spinal localization module, successfully integrated into different object detection models to enhance IVDH detection, achieving an average precision (AP) of up to 75.32%. Additionally, transfer learning was explored to adapt the IVDH detection model for a smaller feline dataset. Overall, this study provides insights into advancing AI for veterinary care, identifying challenges and exploring potential strategies for future development in veterinary radiology.

Abstract Background There is an urgent need for therapeutic strategies for inpatients with severe or critical COVID-19. The evaluation of the clinical benefits of nirmatrelvir and ritonavir (Nmr/r) for these patients beyond five days of symptom onset is insufficient. Methods A new propensity score-matched cohort was constructed by using multicenter data from 6695 adult inpatients with COVID-19 from December 2022 to February 2023 in China after the epidemic control measures were lifted across the country. The severity of disease of the inpatients was based on the tenth trial edition of the Guidelines on the Diagnosis and Treatment of COVID-19 in China. The symptom onset of 1870 enrolled severe or critical inpatients was beyond five days, and they received either Nmr/r plus standard treatment or only standard care. The ratio of patients whose SOFA score improved more than 2 points, crucial respiratory endpoints, changes in inflammatory markers, safety on the seventh day following the initiation of Nmr/r treatment, and length of hospital stay were evaluated. Results In the Nmr/r group, on Day 7, the number of patients with an improvement in SOFA score ≥ 2 was much greater than that in the standard treatment group (P = 0.024) without a significant decrease in glomerular filtration rate (P = 0.815). Additionally, the rate of new intubation was lower (P = 0.004) and the no intubation days were higher (P = 0.003) in the first 7 days in the Nmr/r group. Other clinical benefits were limited. Conclusions Our study may provide new insight that inpatients with severe or critical COVID-19 beyond five days of symptom onset benefit from Nmr/r. Future studies, particularly randomized controlled trials, are necessary to verify the above findings.

Abstract Purpose We describe a surgical technique for ACL reconstruction combined with anterolateral structure reinforcement and report early clinical follow-up results. Methods The semitendinosus and gracilis tendons are braided into 5 strands and the ACL femoral tunnel and tibial tunnel are created. The graft is passed through the tunnel with the use of a traction suture and the tibial end is fixed with absorbable interference screws at 30° of knee flexion. The ACL graft traction suture is used as an anterolateral reconstruction structure to pass through the proximal exit of the ACL femoral tunnel and then through the depth of the iliotibial bundle to the anterior to Gerdy’s tubercle, a bony tunnel is created from the anterior to Gerdy’s tubercle to the goose foot, and the traction suture is passed through this bony tunnel to form a Loop structure at 20° of knee flexion. Between March 2021 and May 2022 IKDC score, Lysholm score, and Tegner score were performed preoperatively and 6–12 months postoperatively in 24 consecutive patients who met the indications for this procedure and underwent surgery. The patient’s maximum flexion angle, the circumference of the thigh, and the stress X-ray between the operated and healthy knee were measured. Results Patients showed significant improvement in IKDC score, Lysholm score and Tegner score at a mean follow-up of 7 months postoperatively compared to preoperatively. No significant increase in anterior tibial displacement was found between the patient’s operated side and the healthy side. Conclusion The Loop technique ACLR combined with ALSA can be used in patients with an ACL tear combined with a high degree of positive pivot shift. The patient’s subjective perception was significantly improved from the preoperative period and knee stability was restored. Level of evidence IV, therapeutic study.

Abstract Background Conventional Mechanical ventilation modes used for individuals suffering from acute respiratory distress syndrome have the potential to exacerbate lung injury through regional alveolar overinflation and/or repetitive alveolar collapse with shearing, known as atelectrauma. Animal studies have demonstrated that airway pressure release ventilation (APRV) offers distinct advantages over conventional mechanical ventilation modes. However, the methodologies for implementing APRV vary widely, and the findings from clinical studies remain controversial. This study (APRVplus trial), aims to assess the impact of an early pathophysiology-driven APRV ventilation approach compared to a low tidal volume ventilation (LTV) strategy on the prognosis of patients with moderate to severe ARDS. Methods The APRVplus trial is a prospective, multicenter, randomized clinical trial, building upon our prior single-center study, to enroll 840 patients from at least 35 hospitals in China. This investigation plans to compare the early pathophysiology-driven APRV ventilation approach with the control intervention of LTV lung-protective ventilation. The primary outcome measure will be all-cause mortality at 28 days after randomization in the intensive care units (ICU). Secondary outcome measures will include assessments of oxygenation, and physiology parameters at baseline, as well as on days 1, 2, and 3. Additionally, clinical outcomes such as ventilator-free days at 28 days, duration of ICU and hospital stay, ICU and hospital mortality, and the occurrence of adverse events will be evaluated. Trial ethics and dissemination The research project has obtained approval from the Ethics Committee of West China Hospital of Sichuan University (2019-337). Informed consent is required. The results will be submitted for publication in a peer-reviewed journal and presented at one or more scientific conferences. Trial registration The study was registered at Clinical Trials.gov (NCT03549910) on June 8, 2018.

IntroductionThe cardiothoracic ratio (CTR) based on postero-anterior chest X-rays (P-A CXR) images is one of the most commonly used cardiac measurement methods and an indicator for initially evaluating cardiac diseases. However, the hearts are not readily observable on P-A CXR images compared to the lung fields. Therefore, radiologists often manually determine the CTR’s right and left heart border points of the adjacent left and right lung fields to the heart based on P-A CXR images. Meanwhile, manual CTR measurement based on the P-A CXR image requires experienced radiologists and is time-consuming and laborious.MethodsBased on the above, this article proposes a novel, fully automatic CTR calculation method based on lung fields abstracted from the P-A CXR images using convolutional neural networks (CNNs), overcoming the limitations to heart segmentation and avoiding errors in heart segmentation. First, the lung field mask images are abstracted from the P-A CXR images based on the pre-trained CNNs. Second, a novel localization method of the heart’s right and left border points is proposed based on the two-dimensional projection morphology of the lung field mask images using graphics.ResultsThe results show that the mean distance errors at the x-axis direction of the CTR’s four key points in the test sets T1 (21 × 512 × 512 static P-A CXR images) and T2 (13 × 512 × 512 dynamic P-A CXR images) based on various pre-trained CNNs are 4.1161 and 3.2116 pixels, respectively. In addition, the mean CTR errors on the test sets T1 and T2 based on four proposed models are 0.0208 and 0.0180, respectively.DiscussionOur proposed model achieves the equivalent performance of CTR calculation as the previous CardioNet model, overcomes heart segmentation, and takes less time. Therefore, our proposed method is practical and feasible and may become an effective tool for initially evaluating cardiac diseases.

Abstract Multidrug-resistant (MDR) Acinetobacter baumannii (A. baumannii) is a formidable pathogen responsible for severe intracranial infections post-craniotomy, exhibiting a mortality rate as high as 71%. Tigecycline (TGC), a broad-spectrum antibiotic, emerged as a potential therapeutic agent for MDR A. baumannii infections. Nonetheless, its clinical application was hindered by a short in vivo half-life and limited permeability through the blood–brain barrier (BBB). In this study, we prepared a novel core–shell nanoparticle encapsulating water-soluble tigecycline using a blend of mPEG-PLGA and PLGA materials. This nanoparticle, modified with a dual-targeting peptide Aβ11 and Tween 80 (Aβ11/T80@CSs), was specifically designed to enhance the delivery of tigecycline to the brain for treating A. baumannii-induced intracranial infections. Our findings demonstrated that Aβ11/T80@CSs nanocarriers successfully traversed the BBB and effectively delivered TGC into the cerebrospinal fluid (CSF), leading to a significant therapeutic response in a model of MDR A. baumannii intracranial infection. This study offers initial evidence and a platform for the application of brain-targeted nanocarrier delivery systems, showcasing their potential in administering water-soluble anti-infection drugs for intracranial infection treatments, and suggesting promising avenues for clinical translation. Graphical abstract

ABSTRACTBrassinosteroid (BR) is a hormone known for its crucial role in seed germination promotion. However, there is a limited understanding of potential application in tobacco seed. In this study, we investigated various active ingredients for seed coating to enhance tobacco seed germination. It revealed that seed coating with 0.0500 ppm BR and priming with 0.01 ppm BR significantly increased seed germination rates, especially in low-temperature conditions. In addition, the combined use BR with priming and seed coating exhibited a substantial improvement in seed germination rates and promoted seedling growth. Through transcriptional analysis, it found that BR treatment regulates the expression of genes associated with DNA synthesis, cell wall organization and glucan metabolic process involved in seed dormancy and seedling growth. Overall, these results provide valuable insights into the utilization of seed coating and priming techniques, particularly with the inclusion of BR, to enhance tobacco seed germination and facilitate seedling emergence.

IntroductionAccurate neurological impairment assessment is crucial for the clinical treatment and prognosis of patients with acute ischemic stroke (AIS). However, the original perfusion parameters lack the deep information for characterizing neurological impairment, leading to difficulty in accurate assessment. Given the advantages of radiomics technology in feature representation, this technology should provide more information for characterizing neurological impairment. Therefore, with its rigorous methodology, this study offers practical implications for clinical diagnosis by exploring the role of ischemic perfusion radiomics features in assessing the degree of neurological impairment.MethodsThis study employs a meticulous methodology, starting with generating perfusion parameter maps through Dynamic Susceptibility Contrast-Perfusion Weighted Imaging (DSC-PWI) and determining ischemic regions based on these maps and a set threshold. Radiomics features are then extracted from the ischemic regions, and the t-test and least absolute shrinkage and selection operator (Lasso) algorithms are used to select the relevant features. Finally, the selected radiomics features and machine learning techniques are used to assess the degree of neurological impairment in AIS patients.ResultsThe results show that the proposed method outperforms the original perfusion parameters, radiomics features of the infarct and hypoxic regions, and their combinations, achieving an accuracy of 0.926, sensitivity of 0.923, specificity of 0.929, PPV of 0.923, NPV of 0.929, and AUC of 0.923, respectively.ConclusionThe proposed method effectively assesses the degree of neurological impairment in AIS patients, providing an objective auxiliary assessment tool for clinical diagnosis.

Background In China, both nirmatrelvir-ritonavir (Paxlovid) and azvudine have been granted approval to treat adult SARS-CoV-2-infected patients with moderate symptoms. Information about the clinical effect of the two available agents among inpatients with severe or critical COVID-19 is scarce.Purpose To compare the clinical outcomes of Paxlovid and azvudine among adult inpatients with severe or critical COVID-19.Method We conducted a retrospective cohort study in two large medical centres after the epidemic control measures were lifted in China. A new propensity score matched-inverse probability of treatment weighting cohort was constructed to evaluate the in-hospital all-cause mortality, hospital length of stay, Sequential Organ Failure Assessment (SOFA) score and safety.Results A total of 955 individuals were in the cohort. The antiviral therapy strategies were decided by the senior physician and the supplies of the pharmacy. A total of 451 patients were in the Paxlovid group, and 504 patients were in the azvudine group. Compared with Paxlovid, the effects of azvudine on in-hospital all-cause mortality were not significantly different, and the OR (95% CI) was 1.084 (0.822 to 1.430), and the average hospital length of stay of patients discharged alive was also similar in the azvudine group, and the difference (day) and (95% CI) was 0.530 (−0.334 to 1.393). After 7 days of therapy, the degree of decline in the SOFA score was greater in the Paxlovid group than in the azvudine group (p

Low-grade appendiceal mucinous neoplasms (LAMNs) are rare and heterogeneous diseases that, despite their increased incidence, are well differentiated, tend to be painless, and histologically lack distinctive invasive features without infiltrative growth, destructive infiltration, or associated pro-fibroproliferative responses. However, the biological behaviour of these tumours is difficult to determine preoperatively or intraoperatively, and the possibility of rupture puts patients at risk for peritoneal pseudomucinous neoplasms (PMPs).Patients with low-grade appendiceal mucinous tumours and peritoneal pseudomucinous tumours experience slow disease progression and are incurable and have a high risk of recurrence, morbidity, and ultimately death, despite the reported 5- and 10-year survival rates of 50–86% and 45–68%, respectively. In this article, we report the case of a 80-year-old male with a giant low-grade appendiceal mucinous tumour associated with a peritoneal pseudomucinous tumour, and discuss the diagnostic and management strategies for giant low-grade appendiceal mucinous tumours in the context of a literature review.

Diagnosing liver disease presents a significant medical challenge in impoverished countries, with over 30 billion individuals succumbing to it each year. Existing models for detecting liver abnormalities suffer from lower accuracy and higher constraint metrics. As a result, there is a pressing need for improved, efficient, and effective liver disease detection methods. To address the limitations of current models, this method introduces a deep liver segmentation and classification system based on a Customized Mask-Region Convolutional Neural Network (cm-RCNN). The process begins with preprocessing the input liver image, which includes Adaptive Histogram Equalization (AHE). AHE helps dehaze the input image, remove color distortion, and apply linear transformations to obtain the preprocessed image. Next, a precise region of interest is segmented from the preprocessed image using a novel deep strategy called cm-RCNN. To enhance segmentation accuracy, the architecture incorporates the ReLU activation function and the modified sigmoid activation function. Subsequently, a variety of features are extracted from the segmented image, including ResNet features, shape features (area, perimeter, approximation, and convex hull), and enhanced median binary pattern. These extracted features are then used to train a hybrid classification model, which incorporates classifiers like SqueezeNet and DeepMaxout models. The final classification outcome is determined by averaging the scores obtained from both classifiers.