The causal relationship between gut microbiota and lower extremity deep vein thrombosis combined with pulmonary embolism.

Background: Over the years, numerous studies have explored the relationship between gut microbiota and lower extremity deep vein thrombosis (LEDVT) and pulmonary embolism (PE). The present study utilized Mendelian randomization (MR) to assess the causal link between gut microbiota and LEDVT combined with PE. Methods: Human gut microbiota genome-wide association study (GWAS) summary data from the MiBioGen consortium (n = 18,340) were utilized. Summary-level data on LEDVT (2,116 cases and 359,078 controls) and LEDVT combined with PE (4,319 cases and 356,875 controls) were obtained from the IEU Open GWAS project. MR analysis was conducted using the inverse variance weighted (IVW) method as the primary analysis. Additionally, MR-Egger, weighted median, weighted mode, and simple mode were employed as supplementary methods. Sensitivity analyses, including tests for heterogeneity and horizontal pleiotropy, were performed. Lastly, reverse MR analysis was performed. Results: The IVW analyses revealed seven causal relationships between genetic liability in the gut microbiota and LEDVT and five causal relationships between genetic liability in the gut microbiota and LEDVT combined with PE. The intersection of these outcomes identified that the genus Butyricicoccus reduced the risk of both LEDVT and LEDVT combined with PE, while the genus Clostridium innocuum increased the risk for both conditions. Conclusion: This study demonstrates that the gut microbiota is causally associated with LEDVT and LEDVT combined with PE. Our findings provide valuable insights into the underlying mechanisms and suggest potential avenues for further clinical investigations of these conditions.

Development and validation of a nomogram-based model to predict combined esophageal injury post-esophagoscopy for esophageal foreign bodies.

OBJECTIVE: The aim of this study is to create and validate a nomogram model for predicting complications of esophageal injury post-esophagoscopy in patients with esophageal foreign bodies (EFB). METHODS: We examined 303 patients who underwent esophagoscopy from January 2019 to December 2022 at a leading hospital in Anhui, known for its expertise in otorhinolaryngology-head and neck surgery. The patients were split into a modeling group and a validation group in a 7:3 ratio. Logistic regression analysis was employed to determine the risk factors for esophageal injury after undergoing esophagoscopy in patients with EFB. Based on these factors, a nomogram risk prediction model was developed and assessed using a goodness of fit test. RESULTS: Logistic regression analysis revealed that the type of foreign body, failure of gastroscopic retrieval, duration of lodgment, lodgment site, and presence of combined cardiovascular and cerebrovascular diseases were significant (p < 0.05) independent risk factors for esophageal injury following esophagoscopy for EFB. The area under the ROC curve for the training set was 0.850, and the Hosmer-Lemeshow goodness of fit test resulted in a p value of 0.908. For the validation set, the area under the ROC curve was 0.848, and the Hosmer-Lemeshow test gave a p value of 0.665. The calibration curve showed a close alignment between the predicted and observed values. CONCLUSION: The type of foreign body, duration of lodgment, lodgment site, previous failure of gastroscopic retrieval, and history of combined cardiovascular and cerebrovascular diseases are significant risk factors for esophageal injury following EFB esophagoscopy. This model accurately quantifies the risk of esophageal injury after EFB esophagoscopy.

Rethinking Delayed Hemodynamic Responses for fNIRS Classification.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology for monitoring cerebral hemodynamic responses. Enhancing fNIRS classification can improve the performance of brain-computer interfaces (BCIs). Currently, deep neural networks (DNNs) do not consider the inherent delayed hemodynamic responses of fNIRS signals, which causes many optimization and application problems. Considering the kernel size and receptive field of convolutions, delayed hemodynamic responses as domain knowledge are introduced into fNIRS classification, and a concise and efficient model named fNIRSNet is proposed. We empirically summarize three design guidelines for fNIRSNet. In subject-specific and subject-independent experiments, fNIRSNet outperforms other DNNs on open-access datasets. Specifically, fNIRSNet with only 498 parameters is 6.58% higher than convolutional neural network (CNN) with millions of parameters on mental arithmetic tasks and the floating-point operations (FLOPs) of fNIRSNet are much lower than CNN. Therefore, fNIRSNet is friendly to practical applications and reduces the hardware cost of BCI systems. It may inspire more research on knowledge-driven models for fNIRS BCIs. Code is available at https://github.com/wzhlearning/fNIRSNet.

Deep learning-guided postoperative pain assessment in children.

ABSTRACT: Current automated pain assessment methods only focus on infants or youth. They are less practical because the children who suffer from postoperative pain in clinical scenarios are in a wider range of ages. In this article, we present a large-scale Clinical Pain Expression of Children (CPEC) dataset for postoperative pain assessment in children. It contains 4104 preoperative videos and 4865 postoperative videos of 4104 children (from 0 to 14 years of age), which are collected from January 2020 to December 2020 in Anhui Provincial Children's Hospital. Moreover, inspired by the dramatic successful applications of deep learning in medical image analysis and emotion recognition, we develop a novel deep learning-based framework to automatically assess postoperative pain according to the facial expression of children, namely Children Pain Assessment Neural Network (CPANN). We train and evaluate the CPANN with the CPEC dataset. The performance of the framework is measured by accuracy and macro-F1 score metrics. The CPANN achieves 82.1% accuracy and 73.9% macro-F1 score on the testing set of CPEC. The CPANN is faster, more convenient, and more objective compared with using pain scales according to the specific type of pain or children's condition. This study demonstrates the effectiveness of deep learning-based method for automated pain assessment in children.

Newly discovered circRNAs in rheumatoid arthritis, with special emphasis on functional roles in inflammatory immunity.

Circular RNA (circRNA) is a unique type of endogenous RNA. It does not have free 3 'or 5' ends, but forms covalently closed continuous rings. Rheumatoid arthritis (RA) is a common chronic autoimmune joint disease, characterized by chronic inflammation of the joint synovial membrane, joint destruction, and the formation of pannus. Although the pathogenesis of rheumatoid arthritis remains incompletely understood, a growing amount of research shows that circRNA has a close relationship with RA. Researchers have found that abnormally expressed circRNAs may be associated with the occurrence and development of RA. This article reviews the inflammatory immune, functions, mechanisms, and values of the circRNAs in RA to provide new ideas and novel biomarkers for the diagnosis and treatment of RA.

Collagen I in the Hip Capsule Plays a Role in Postoperative Clinical Function in Patients With Developmental Dysplasia of the Hip.

The aims of the present study is to evaluate the roles of collagen I and III in the hip capsule in the postoperative clinical function of patients with developmental dysplasia of the hip (DDH). Hip capsules from 155 hips of 120 patients were collected during surgery. The patients were divided into three groups according to age: I: 2-3.5 years; II: 3.5-5 years; and III: 5-6 years. Patient clinical function and radiographic outcomes were evaluated with the McKay scores and Severin classification. The expression of collagen I and III was detected through immunohistochemistry and quantitative reverse transcription polymerase chain reaction (RT-PCR) and analyzed according to age, sex, degree of dislocation and McKay classification. All patients received open reduction and pelvic osteotomy and/or femoral shortening osteotomy and achieved good results on the basis of postoperative X-ray imaging. The average follow-up time was 3.4 years (range 2-4.3 years). There were no changes in the expression of collagen III in the different groups. The expression of collagen I according to age and sex was not significantly different. Lower expression of collagen I was observed in DDH patients with a higher degree of dislocation according to the Tonnis grade. The highest expression of collagen I was detected in the group with poor clinical function according to the McKay classification. Collagen I is correlated with the degree of dislocation and is a risk factor for poor clinical function in DDH patients. Collagen I is correlated with the degree of hip dislocation and poor clinical function in DDH patients.

Application of deep-learning-based artificial intelligence in acetabular index measurement.

Objective: To construct an artificial intelligence system to measure acetabular index and evaluate its accuracy in clinical application. Methods: A total of 10,219 standard anteroposterior pelvic radiographs were collected retrospectively from April 2014 to December 2018 in our hospital. Of these, 9,219 radiographs were randomly selected to train and verify the system. The remaining 1,000 radiographs were used to compare the system's and the clinicians' measurement results. All plain pelvic films were labeled by an expert committee through PACS system based on a uniform standard to measure acetabular index. Subsequently, eight other clinicians independently measured the acetabular index from 200 randomly selected radiographs from the test radiographs. Bland-Altman test was used for consistency analysis between the system and clinician measurements. Results: The test set included 1,000 cases (2,000 hips). Compared with the expert committee measurement, the 95% limits of agreement (95% LOA) of the system was -4.02° to 3.45° (bias = -0.27°, P < 0.05). The acetabular index measured by the system within all age groups, including normal and abnormal groups, also showed good credibility according to the Bland-Altman principle. Comparison of the measurement evaluations by the system and eight clinicians vs. that of, the expert committee, the 95% LOA of the clinician with the smallest measurement error was -2.76° to 2.56° (bias = -0.10°, P = 0.126). The 95% LOA of the system was -0.93° to 2.86° (bias = -0.03°, P = 0.647). The 95% LOA of the clinician with the largest measurement error was -3.41° to 4.25° (bias = 0.42°, P < 0.05). The measurement error of the system was only greater than that of a senior clinician. Conclusion: The newly constructed artificial intelligence system could quickly and accurately measure the acetabular index of standard anteroposterior pelvic radiographs. There is good data consistency between the system in measuring standard anteroposterior pelvic radiographs. The accuracy of the system is closer to that of senior clinicians.

The Research Progress of Exosomes in Osteoarthritis, With Particular Emphasis on the Mediating Roles of miRNAs and lncRNAs.

Osteoarthritis (OA) is a kind of degenerative disease, which is caused by many factors such as aging, obesity, strain, trauma, congenital joint abnormalities, joint deformities. Exosomes are mainly derived from the invagination of intracellular lysosomes, which are released into the extracellular matrix after fusion of the outer membrane of multi vesicles with the cell membrane. Exosomes mediate intercellular communication and regulate the biological activity of receptor cells by carrying non-coding RNA, long noncoding RNAs (lncRNAs), microRNAs (miRNAs), proteins and lipids. Evidences show that exosomes are involved in the pathogenesis of OA. In view of the important roles of exosomes in OA, this paper systematically reviewed the roles of exosomes in the pathogenesis of OA, including the roles of exosomes in OA diagnosis, the regulatory mechanisms of exosomes in the pathogenesis, and the intervention roles of exosomes in the treatment of OA. Reviewing the roles of exosomes in OA will help to clarify the pathogenesis of OA and explore new diagnostic biomarkers and therapeutic targets.

Blood Urea Nitrogen to Serum Albumin Ratio Independently Predicts Mortality in Critically Ill Patients With Acute Pulmonary Embolism.

Acute pulmonary embolism (APE) is one of the prominent causes of death in patients with cardiovascular disease. Currently, reliable biomarkers to predict the prognosis of patients with APE are limited. The present study aimed to investigate the association of blood urea nitrogen to serum albumin (B/A) ratio and intensive care unit (ICU) mortality in critically ill patients with APE. A retrospective cohort study was performed using data extracted from a freely accessible critical care database (MIMIC-III). Adult (≥18 years) patients of first ICU admission with a primary diagnosis of APE in the database were enrolled in the study. The primary endpoint was the ICU mortality rate while the 28-day mortality after ICU admission was the secondary endpoint. The data of survivors and non-survivors were compared. A total of 1048 patients with APE were enrolled in this study, of which 131 patients died in ICU and 169 patients died within 28 days after ICU admission. The B/A ratio in the non-survivors group was significantly higher compared to the survivors group (P < 0.001). The multivariate analysis revealed that the B/A ratio was an independent predictor of ICU mortality (odds ratio [OR] 1.10, 95% CI 1.07-1.14, P < 0.001) and all-cause mortality within 28 days after ICU admission (hazard ratio [HR] 1.07, 95% CI 1.05-1.09, P < 0.001) in APE patients. The B/A ratio showed a greater area under the curve (AUC) of ICU mortality prediction (0.80; P < 0.001) than simplified acute physiology score II (SAPSII) (0.79), systemic inflammatory response syndrome score (SIRS) (0.62), acute physiology score III (APSIII) (0.76) and sequential organ failure assessment (SOFA) score (0.71). The B/A ratio could be a simple and useful prognostic tool to predict mortality in critically ill patients with APE.

Clinical application of artificial intelligence-assisted diagnosis using anteroposterior pelvic radiographs in children with developmental dysplasia of the hip.

AIMS: The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. METHODS: In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Clinicians labelled each radiograph using a uniform standard method. Radiographs were grouped according to age and into 'dislocation' (dislocation and subluxation) and 'non-dislocation' (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis. The deep learning system was trained and optimized using 9,081 radiographs; 1,138 test radiographs were then used to compare the diagnoses made by deep learning system and clinicians. The accuracy of the deep learning system was determined using a receiver operating characteristic curve, and the consistency of acetabular index measurements was evaluated using Bland-Altman plots. RESULTS: In all, 1,138 patients (242 males; 896 females; mean age 1.5 years (SD 1.79; 0 to 10) were included in this study. The area under the receiver operating characteristic curve, sensitivity, and specificity of the deep learning system for diagnosing hip dislocation were 0.975, 276/289 (95.5%), and 1,978/1,987 (99.5%), respectively. Compared with clinical diagnoses, the Bland-Altman 95% limits of agreement for acetabular index, as determined by the deep learning system from the radiographs of non-dislocated and dislocated hips, were -3.27° - 2.94° and -7.36° - 5.36°, respectively (p < 0.001). CONCLUSION: The deep learning system was highly consistent, more convenient, and more effective for diagnosing DDH compared with clinician-led diagnoses. Deep learning systems should be considered for analysis of anteroposterior pelvic radiographs when diagnosing DDH. The deep learning system will improve the current artificially complicated screening referral process. Cite this article: Bone Joint J 2020;102-B(11):1574-1581.