Adverse complications of cervical spinal fusion in patients with different types of diabetes mellitus: A retrospective nationwide inpatient sample database cross-sectional study.

BACKGROUND: Diabetes mellitus (DM) is a prevalent chronic condition that influences spine surgery outcomes. The impact of type Ⅰ and type Ⅱ DM on adverse postoperative outcomes, mortality, prolonged length of stay (LOS), and increased in-hospital costs following cervical fusion surgery remains unclear in the past decade. This study aims to determine the specific effect of different classifications of DM on postoperative complications in patients experiencing cervical fusion surgery. METHOD: Data from the Nationwide Inpatient Sample database was acquired between 2010 and 2019. Patients experiencing cervical fusion were included and classified as having type I DM, type II DM, or neither. Patient demographics, hospital characteristics, operative variables, comorbidities, complications, and other postoperative outcomes were assessed. Propensity score matching analysis was used to balance baseline differences. Univariate and multivariate logistic regression were employed to determine the risk of postoperative outcomes in patients with different classifications of DM. RESULT: A total of 267,174 cervical spinal fusions were identified (224,255 were patients without DM, 670 patients had type I DM, and 42,249 patients had type II DM). After propensity score matching, the multivariate analysis of non-DM and type I DM patients shows significant difference in pneumonia (P=0.020). However, type Ⅱ DM served as an independent predictor of an increased risk of acute cerebrovascular disease (P=0.001), acute myocardial infarction (P=0.014), pneumonia (P=0.045), continuous trauma ventilation (P=0.016), chest pain (P<0.001), urinary tract infection (P<0.001), transfusion (P=0.005) and dysphagia (P=0.013), prolonged LOS (P<0.001) and increased costs (P=0.008). CONCLUSION: Using non-DM patients as a reference, type II DM group demonstrated a higher risk of postoperative complications than type I DM group among patients receiving cervical fusion surgery. This vital distinction could enhance risk stratification and guidance for patients diagnosed with DM before cervical fusion surgery.

Source and succession of microbial communities and tetramethylpyrazine during the brewing process of compound-flavor Baijiu.

Pyrazines are important flavor components and healthy active components in Baijiu, which including tetramethylpyrazine (TTMP). During the brewing process, the traceability of microbial communities and the content distribution characteristics of TTMP are important for improving the quality and style characteristics of compound-flavored Baijiu (CFB). However, the traceability analysis of microorganisms in fermented grains (FG)-used in the production of CFB-lacks quantitative and systematic evaluation. In this study, the microbial communities and TTMP content of Jiuqu (JQ), Liangpei (LP), FG, and pit mud (CP) used in CFB production were characterized; further, coordinate and discriminant analyses were employed to determine differences in microbial communities. Additionally, traceability and correlation analyses were performed to reveal the origin of microbial communities in FG. The source, content, and distribution characteristics of TTMP based on the brewing process have also been discussed. The results showed that most of the bacterial and fungal communities at different levels of FG came from other sources, and the microorganisms of Cladosporium, Acetobacter, Aspergillus, Methanosarcina, and Bacillus were considered have a osculating correlations with TTMP content of FG. Taken together, this study provides insights into the origin of microbial communities in FG and the distribution characteristics of TTMP based on the CFB brewing process. The current findings are conducive for optimizing the fermentation process and improving the quality and style characteristics of CFB.

Baseline Performance of Ultrasound-Based Strategies in Breast Cancer Screening Among Chinese Women.

RATIONALE AND OBJECTIVE: There is a notable absence of robust evidence on the efficacy of ultrasound-based breast cancer screening strategies, particularly in populations with a high prevalence of dense breasts. Our study addresses this gap by evaluating the effectiveness of such strategies in Chinese women, thereby enriching the evidence base for identifying the most efficacious screening approaches for women with dense breast tissue. METHODS: Conducted from October 2018 to August 2022 in Central China, this prospective cohort study enrolled 8996 women aged 35-64 years, divided into two age groups (35-44 and 45-64 years). Participants were screened for breast cancer using hand-held ultrasound (HHUS) and automated breast ultrasound system (ABUS), with the older age group also receiving full-field digital mammography (FFDM). The Breast Imaging Reporting and Data System (BI-RADS) was employed for image interpretation, with abnormal results indicated by BI-RADS 4/5, necessitating a biopsy; BI-RADS 3 required follow-up within 6-12 months by primary screening strategies; and BI-RADS 1/2 were classified as negative. RESULTS: Among the screened women, 29 cases of breast cancer were identified, with 4 (1.3‰) in the 35-44 years age group and 25 (4.2‰) in the 45-64 years age group. In the younger age group, HHUS and ABUS performed equally well, with no significant difference in their AUC values (0.8678 vs. 0.8679, P > 0.05). For the older age group, ABUS as a standalone strategy (AUC 0.9935) and both supplemental screening methods (HHUS with FFDM, AUC 0.9920; ABUS with FFDM, AUC 0.9928) outperformed FFDM alone (AUC 0.8983, P < 0.05). However, there was no significant difference between HHUS alone and FFDM alone (AUC 0.9529 vs. 0.8983, P > 0.05). CONCLUSION: The findings indicate that both HHUS and ABUS exhibit strong performance as independent breast cancer screening strategies, with ABUS demonstrating superior potential. However, the integration of FFDM with these ultrasound techniques did not confer a substantial improvement in the overall effectiveness of the screening process.

GloEC: a hierarchical-aware global model for predicting enzyme function.

The annotation of enzyme function is a fundamental challenge in industrial biotechnology and pathologies. Numerous computational methods have been proposed to predict enzyme function by annotating enzyme labels with Enzyme Commission number. However, the existing methods face difficulties in modelling the hierarchical structure of enzyme label in a global view. Moreover, they haven't gone entirely to leverage the mutual interactions between different levels of enzyme label. In this paper, we formulate the hierarchy of enzyme label as a directed enzyme graph and propose a hierarchy-GCN (Graph Convolutional Network) encoder to globally model enzyme label dependency on the enzyme graph. Based on the enzyme hierarchy encoder, we develop an end-to-end hierarchical-aware global model named GloEC to predict enzyme function. GloEC learns hierarchical-aware enzyme label embeddings via the hierarchy-GCN encoder and conducts deductive fusion of label-aware enzyme features to predict enzyme labels. Meanwhile, our hierarchy-GCN encoder is designed to bidirectionally compute to investigate the enzyme label correlation information in both bottom-up and top-down manners, which has not been explored in enzyme function prediction. Comparative experiments on three benchmark datasets show that GloEC achieves better predictive performance as compared to the existing methods. The case studies also demonstrate that GloEC is capable of effectively predicting the function of isoenzyme. GloEC is available at: https://github.com/hyr0771/GloEC.

Relationship between preoperative psychological stress and short-term prognosis in elderly patients with femoral neck fracture.

BACKGROUND: Older adults are at high risk of femoral neck fractures (FNFs). Elderly patients face and adapt to significant psychological burdens, resulting in different degrees of psychological stress response. Total hip replacement is the preferred treatment for FNF in elderly patients; however, some patients have poor postoperative prognoses, and the underlying mechanism is unknown. We speculated that the postoperative prognosis of elderly patients with FNF may be related to preoperative psychological stress. AIM: To explore the relationship between preoperative psychological stress and the short-term prognosis of elderly patients with FNF. METHODS: In this retrospective analysis, the baseline data, preoperative 90-item Symptom Checklist score, and Harris score within 6 months of surgery of 120 elderly patients with FNF who underwent total hip arthroplasty were collected. We analyzed the indicators of poor short-term postoperative prognosis and the ability of the indicators to predict poor prognosis and compared the correlation between the indicators and the Harris score. RESULTS: Anxiety, depression, garden classification of FNF, cause of fracture, FNF reduction quality, and length of hospital stay were independent influencing factors for poor short-term postoperative prognoses in elderly patients with FNF (P < 0.05). The areas under the curve for anxiety, depression, and length of hospital stay were 0.742, 0.854, and 0.749, respectively. The sensitivities of anxiety, depression, garden classification of FNF, and prediction of the cause of fracture were 0.857, 0.786, 0.821, and 0.821, respectively. The specificities of depression, FNF quality reduction, and length of hospital stay were the highest at 0.880, 0.783, and 0.761, respectively. Anxiety, depression, and somatization scores correlated moderately with Harris scores (r = -0.523, -0.625, and -0.554; all P < 0.001). CONCLUSION: Preoperative anxiety, depression, and somatization are correlated with poor short-term prognosis in elderly patients with FNF and warrant consideration.

Location of Phosphorylation Sites within Long Polypeptide Chains by Binder-Assisted Nanopore Detection.

The detection and mapping of protein phosphorylation sites are essential for understanding the mechanisms of various cellular processes and for identifying targets for drug development. The study of biopolymers at the single-molecule level has been revolutionized by nanopore technology. In this study, we detect protein phosphorylation within long polypeptides (>700 amino acids), after the attachment of binders that interact with phosphate monoesters; electro-osmosis is used to drive the tagged chains through engineered protein nanopores. By monitoring the ionic current carried by a nanopore, phosphorylation sites are located within individual polypeptide chains, providing a valuable step toward nanopore proteomics.

Patient Preference for Catheter-Based Hypertension Therapy and Subgroup Analysis: A Pilot Study Based on Taiwan Consensus on Renal Denervation.

Ojective: To understand hypertensive patients' preference for catheter-based therapy to manage hypertension. Methods: Survey data regarding catheter-based therapies performed at MacKay Memorial Hospital in Taipei, Taiwan, between 2019-2020 were analyzed. The questionnaire was circulated either in the clinics or during admission. A total of 46 patients completed the questionnaire. Results: A total of 46 patients (mean age 53.4 ± 13.5 years, 78.3% male) completed the questionnaire. In subgroup analysis according to Taiwan renal denervation (RDN) consensus, patients with drug intolerance (61.8% vs. 31.3%, p = 0.02) were more likely to choose RDN. Moreover, although lacking statistical significance, it is noteworthy that numerically more of the resistant hypertension group (55.6% vs. 28.0%, p = 0.09) and non-adherence group (38.5% vs. 30.0%, p = 0.20) were willing to undergo RDN. Conversely, numerically fewer patients with hypertension-mediated organ damage accepted RDN compared to those who did not have hypertension-mediated organ damage (26.1% vs. 43.5%, p = 0.21), although this disparity did not reach statistical significance. Conclusions: Approximately one-third of the patients expressed interest in considering RDN in this study. The most influential factor in patients' preference for RDN was drug intolerance due to medication-related side effects.

Fermented Aronia melanocarpa pomace improves the nutritive value of eggs, enhances ovarian function, and reshapes microbiota abundance in aged laying hens.

Introduction: There is a decline in the quality and nutritive value of eggs in aged laying hens. Fruit pomaces with high nutritional and functional values have gained interest in poultry production to improve the performance. Methods: The performance, egg nutritive value, lipid metabolism, ovarian health, and cecal microbiota abundance were evaluated in aged laying hens (320 laying hens, 345-day-old) fed on a basal diet (control), and a basal diet inclusion of 0.25%, 0.5%, or 1.0% fermented Aronia melanocarpa pomace (FAMP) for eight weeks. Results: The results show that 0.5% FAMP reduced the saturated fatty acids (such as C16:0) and improved the healthy lipid indices in egg yolks by decreasing the atherogenicity index, thrombogenic index, and hypocholesterolemia/hypercholesterolemia ratio and increasing health promotion index and desirable fatty acids (P < 0.05). Additionally, FAMP supplementation (0.25%-1.0%) increased (P < 0.05) the ovarian follicle-stimulating hormone, luteinizing hormone, and estrogen 2 levels, while 1.0% FAMP upregulated the HSD3B1 expression. The expression of VTG II and ApoVLDL II in the 0.25% and 0.5% FAMP groups, APOB in the 0.5% FAMP group, and ESR2 in the 1% FAMP group were upregulated (P < 0.05) in the liver. The ovarian total antioxidant capacity was increased (P < 0.05) by supplementation with 0.25%-1.0% FAMP. Dietary 0.5% and 1.0% FAMP downregulated (P < 0.05) the Keap1 expression, while 1.0% FAMP upregulated (P < 0.05) the Nrf2 expression in the ovary. Furthermore, 1.0% FAMP increased cecal acetate, butyrate, and valerate concentrations and Firmicutes while decreasing Proteobacteria (P < 0.05). Conclusion: Overall, FAMP improved the nutritive value of eggs in aged laying hens by improving the liver-blood-ovary function and cecal microbial and metabolite composition, which might help to enhance economic benefits.

Epidemiology of carbapenem-resistant Klebsiella pneumoniae in China and the evolving trends of predominant clone ST11: a multicentre, genome-based study.

OBJECTIVES: Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a major nosocomial infectious pathogen with rapidly increasing prevalence. The genomic epidemiological characteristics of CRKP nationwide, especially the evolving trends within the predominant clones, should be evaluated clearly. METHODS: We collected 3415 K. pneumoniae strains from 28 hospitals across China. Antimicrobial susceptibility testing and WGS were performed. Subsequent genomic analyses, including sequence typing, K-locus (KL) identification, antimicrobial resistance gene screening, and virulence score assessment were performed. The phylogenetic relationship of clonal group 11 was determined based on core-genome analysis, and the presence of the pLVPK-like virulence plasmid in ST11 isolates was confirmed using plasmid core-gene analysis. Additionally, the trends of the ST11 lineage with different KL types on a global scale were investigated using Beast2. RESULTS: Of the K. pneumoniae strains, 708 were identified as CRKP isolates (20.7%), of which 97.7% were MDR. ST11 was the predominant clone, and KPC-2 was the prevalent carbapenemase in China, although the prevalence of specific clones and carbapenemases varied by geographic region. Among ST11 isolates, KL47 and KL64 were the predominant KL types, and KL64 gradually replaced KL47, with a higher percentage of KL64 isolates harbouring the pLVPK-like plasmid. Global genome data showed a significant increase in the effective population size of KL64 over the last 5 years. CONCLUSIONS: The prevalence of CRKP was very high in certain regions in China. The increasing convergence of virulence and resistance, particularly in ST11-KL64 isolates, should be given more attention and further investigation.

ß-catenin mediates monocrotaline-induced pulmonary hypertension via glycolysis in rats.

BACKGROUND: Metabolic abnormalities and immune inflammation are deeply involved in pulmonary vascular remodelling and the development of pulmonary hypertension (PH). However, the regulatory mechanisms of glycolysis in macrophages are still elusive. Cumulative evidence indicates that ß-catenin plays a crucial role in metabolic reprogramming. This study aimed to investigate the effect of ß-catenin on macrophage glycolysis in PH. METHODS: LPS-induced BMDMs were generated via in vitro experiments. A monocrotaline (MCT)-induced PH rat model was established, and the ß-catenin inhibitor XAV939 was administered in vivo. The role of ß-catenin in glycolysis was analysed. The degree of pulmonary vascular remodelling was measured. RESULTS: ß-catenin was significantly increased in both in vitro and in vivo models. In LPS-induced BMDMs, ß-catenin increased the levels of hexokinase 2 (HK2), phosphofructokinase (PFK), M2-pyruvate kinase (PKM2), lactate dehydrogenase (LDH), and lactate (LA) and the expression of inflammatory cytokines and promoted PASMC proliferation and migration in vitro. XAV939 decreased the level of glycolysis and downregulated the expression of inflammatory cytokines in vivo. MCT promoted pulmonary arterial structural remodelling and right ventricular hypertrophy, and XAV939 alleviated these changes. CONCLUSIONS: Our findings suggest that ß-catenin is involved in the development of PH by promoting glycolysis and the inflammatory response in macrophages. Inhibition of ß-catenin could improve the progression of PH.

Distinct characteristics of the gut virome in patients with osteoarthritis and gouty arthritis.

BACKGROUND/PURPOSE(S): The gut microbiota and its metabolites play crucial roles in pathogenesis of arthritis, highlighting gut microbiota as a promising avenue for modulating autoimmunity. However, the characterization of the gut virome in arthritis patients, including osteoarthritis (OA) and gouty arthritis (GA), requires further investigation. METHODS: We employed virus-like particle (VLP)-based metagenomic sequencing to analyze gut viral community in 20 OA patients, 26 GA patients, and 31 healthy controls, encompassing a total of 77 fecal samples. RESULTS: Our analysis generated 6819 vOTUs, with a considerable proportion of viral genomes differing from existing catalogs. The gut virome in OA and GA patients differed significantly from healthy controls, showing variations in diversity and viral family abundances. We identified 157 OA-associated and 94 GA-associated vOTUs, achieving high accuracy in patient-control discrimination with random forest models. OA-associated viruses were predicted to infect pro-inflammatory bacteria or bacteria associated with immunoglobulin A production, while GA-associated viruses were linked to Bacteroidaceae or Lachnospiraceae phages. Furthermore, several viral functional orthologs displayed significant differences in frequency between OA-enriched and GA-enriched vOTUs, suggesting potential functional roles of these viruses. Additionally, we trained classification models based on gut viral signatures to effectively discriminate OA or GA patients from healthy controls, yielding AUC values up to 0.97, indicating the clinical utility of the gut virome in diagnosing OA or GA. CONCLUSION: Our study highlights distinctive alterations in viral diversity and taxonomy within gut virome of OA and GA patients, offering insights into arthritis etiology and potential treatment and prevention strategies.

Profiling the Gut Microbiota in Obese Children with Formula Feeding in Early Life and Selecting Strains against Obesity.

Formula feeding, obesity and the gut microbiota are closely related. The present investigation explored the profiles of the intestinal microbiota in obese children over 5 years old with formula feeding in early life. We identified functional bacteria with anti-obesity potential through in vitro and in vivo experiments, elucidating their mechanisms. The results indicated that, in the group of children over 5 years old who were fed formula in early life, obese children exhibited distinct gut microbiota, which were characterized by diminished species diversity and reduced Bifidobacterium levels compared to normal-weight children. As a result, Lactobacillus acidophilus H-68 (H-68) was isolated from the feces of the N-FF group and recognized as a promising candidate. H-68 demonstrated the ability to stimulate cholecystokinin (CCK) secretion in STC-1 cells and produce bile salt hydrolase. In vivo, H-68 promoted CCK secretion, suppressing food intake, and regulated bile acid enterohepatic circulation, leading to increased deoxycholic acid and lithocholic acid levels in the ileum and liver. This regulation effectively inhibited the diet-induced body weight and body fat gain, along with the liver fat deposition. In conclusion, H-68 was recognized for its prospective anti-obesity impact, signifying an auspicious pathway for forthcoming interventions targeted at averting pediatric obesity in formula-fed children.

Similarity-guided graph contrastive learning for lncRNA-disease association prediction.

The increasing research evidence indicates that long non-coding RNAs (lncRNAs) play important roles in regulating biological processes and are closely associated with many human diseases. Computational methods have emerged as indispensable tools for identifying associations between long non-coding RNA (lncRNA) and diseases, primarily due to the time-consuming and costly nature of traditional biological experiments. Given the scarcity of verified lncRNA-disease associations, the intensifying focus on deep learning is playing a crucial role in refining the accuracy of predictive models. Moreover, the contrastive learning method exhibits a clear advantage in situations where data is scarce or annotation costs are high. In this paper, we leverage the advantages of graph neural networks and contrastive learning to innovatively propose a similarity-guided graph contrastive learning (SGGCL) model for predicting lncRNA-disease associations. In the SGGCL model, we employ a novel similarity-guided graph data augmentation method to generate high-quality positive and negative sample pairs, addressing the scarcity of verified data. Additionally, we utilize the RWR algorithm and a graph convolutional neural network for contrastive learning, facilitating the capture of global topology and high-level node embeddings. The experimental results on several datasets demonstrate the superior predictive performance and scalability of our method in lncRNA-disease association prediction compared to state-of-the-art methods.

Artificial Intelligence-Assisted Prescription Determination for Orthokeratology Lens Fitting: From Algorithm to Clinical Practice.

OBJECTIVES: To explore the potential of artificial intelligence (AI) to assist prescription determination for orthokeratology (OK) lenses. METHODS: Artificial intelligence algorithm development followed by a real-world trial. A total of 11,502 OK lenses fitting records collected from seven clinical environments covering major brands. Records were randomly divided in a three-way data split. Cross-validation was used to identify the most accurate algorithm, followed by an evaluation using an independent test data set. An online AI-assisted system was implemented and assessed in a real-world trial involving four junior and three senior clinicians. RESULTS: The primary outcome measure was the algorithm's accuracy (ACC). The ACC of the best performance of algorithms to predict the targeted reduction amplitude, lens diameter, and alignment curve of the prescription was 0.80, 0.82, and 0.83, respectively. With the assistance of the AI system, the number of trials required to determine the final prescription significantly decreased for six of the seven participating clinicians (all P <0.01). This reduction was more significant among junior clinicians compared with consultants (0.76±0.60 vs. 0.32±0.60, P <0.001). Junior clinicians achieved clinical outcomes comparable to their seniors, as 93.96% (140/149) and 94.44% (119/126), respectively, of the eyes fitted achieved unaided visual acuity no worse than 0.8 ( P =0.864). CONCLUSIONS: AI can improve prescription efficiency and reduce discrepancies in clinical outcomes among clinicians with differing levels of experience. Embedment of AI in practice should ultimately help lessen the medical burden and improve service quality for myopia boom emerging worldwide.

Assessment of microorganisms in drinking water disinfected by catalytic ozonation with fluorinated ceramic honeycomb and NaClO disinfectants under laboratory and pilot conditions.

While sodium hypochlorite (NaClO) has long been used to disinfect drinking water, concerns have risen over its use due to causing potentially hazardous byproducts. Catalytic ozonation with metal-free catalysts has attracted increasing attention to eliminate the risk of secondary pollution of byproducts in water treatment. Here, we compared the disinfection efficiency and microbial community of catalytic ozone with a type of metal-free catalyst fluorinated ceramic honeycomb (FCH) and NaClO disinfectants under laboratory- and pilot-scale conditions. Under laboratory conditions, the disinfection rate of catalytic ozonation was 3∼6-fold that of ozone when the concentration of Escherichia coli was 1 × 106 CFU/ml, and all E. coli were killed within 15 s. However, 0.65 mg/L NaClO retained E. coli after 30 min using the traditional culturable approach. The microorganism inactivation results of raw reservoir water disinfected by catalytic ozonation and ozonation within 15 s were incomparable based on the cultural method. In pilot-scale testing, catalytic ozonation inactivated all environmental bacteria within 4 min, while 0.65 mg/L NaClO could not achieve this success. Both catalytic ozonation and NaClO-disinfected methods significantly reduced the number of microorganisms but did not change the relative abundances of different species, i.e., bacteria, viruses, eukaryotes, and archaea, based on metagenomic analyses. The abundance of virulence factors (VFs) and antimicrobial resistance genes (ARGs) was detected few in catalytic ozonation, as determined by metagenomic sequencing. Some VFs or ARGs, such as virulence gene 'FAS-II' which was hosted by Mycobacterium_tuberculosis, were detected solely by the NaClO-disinfected method. The enriched genes and pathways of cataO3-disinfected methods exhibited an opposite trend, especially in human disease, compared with NaClO disinfection. These results indicated that the disinfection effect of catalytic ozone is superior to NaClO, this finding contributed to the large-scale application of catalytic ozonation with FCH in practical water treatment.

Chinese herb ultrafine powder supplementation improves egg nutritional value and quality in laying hens.

This study evaluates the effects of dietary Chinese herb ultrafine powder (CHUP) supplementation in late-phase laying hens on the quality and nutritional values of eggs. A total of 576 Xinyang black-feather laying hens (300-day-old) were randomly allocated into eight groups for a 120-day feeding trial. Each group contained eight replicates with nine hens per replicate. The experimental groups included the control (basal diet) and different levels of CHUP groups (details in 'Materials and methods'). The results showed that the eggshell strength was increased (p < 0.05) in the L, LF, L-LF, L-T, and LF-T groups on day 60 of the trial. In addition, the plasma estradiol level in the L-LF, LF-T, and L-LF-T groups and unsaturated fatty acids concentrations in egg yolk of the CHUP groups (except LF-T group) were increased, whereas total cholesterol (T, L-LF, L-T, and L-LF-T groups) in egg yolk and the atherogenicity (T, L-T, and L-LF-T groups) and thrombogenicity (T, L-LF, L-T, and L-LF-T groups) indexes were decreased (p < 0.05) on day 60 of the trial compared with the control group. Moreover, bitter amino acids in egg albumen were decreased (p < 0.05) in the L-LF group on day 60 and the L-LF-T group on day 120 of the trial. Collectively, these findings indicate that dietary CHUP supplementation could improve eggshell quality and increase plasma reproductive hormone, fatty acid and amino acid composition, and nutritional values of eggs, especially L-LF and L-LF-T.

Desmoplastic fibroma in a child: a 9-year follow-up case report.

BACKGROUND: Desmoplastic fibroma is an extremely rare primary bone tumor. Its characteristic features include bone destruction accompanied by the formation of soft tissue masses. This condition predominantly affects individuals under the age of 30. Since its histology is similar to desmoid-type fibromatosis, an accurate diagnosis before operation is difficult. Desmoplastic fibroma is resistant to chemotherapy, and the efficacy of radiotherapy is uncertain. Surgical excision is preferred for treatment, but it entails high recurrence. Further, skeletal reconstruction post-surgery is challenging, especially in pediatric cases. CASE PRESENTATION: Nine years ago, a 14-year-old male patient presented with a 4-year history of progressive pain in his left wrist. Initially diagnosed as fibrous dysplasia by needle biopsy, the patient underwent tumor resection followed by free vascularized fibular proximal epiphyseal transfer for wrist reconstruction. However, a histological examination confirmed a diagnosis of desmoplastic fibroma. The patient achieved bone union and experienced a recurrence in the ipsilateral ulna 5 years later, accompanied by a wrist deformity. He underwent a second tumor resection and wrist arthrodesis in a single stage. The most recent annual follow-up was in September 2023; the patient had no recurrence and was satisfied with the surgery. CONCLUSIONS: Desmoplastic fibroma is difficult to diagnose and treat, and reconstruction surgery after tumor resection is challenging. Close follow-up by experienced surgeons may be beneficial for prognosis.

LGCDA: Predicting CircRNA-Disease Association Based on Fusion of Local and Global Features.

CircRNA has been shown to be involved in the occurrence of many diseases. Several computational frameworks have been proposed to identify circRNA-disease associations. Despite the existing computational methods have obtained considerable successes, these methods still require to be improved as their performance may degrade due to the sparsity of the data and the problem of memory overflow. We develop a novel computational framework called LGCDA to predict circRNA-disease associations by fusing local and global features to solve the above mentioned problems. First, we construct closed local subgraphs by using k-hop closed subgraph and label the subgraphs to obtain rich graph pattern information. Then, the local features are extracted by using graph neural network (GNN). In addition, we fuse Gaussian interaction profile (GIP) kernel and cosine similarity to obtain global features. Finally, the score of circRNA-disease associations is predicted by using the multilayer perceptron (MLP) based on local and global features. We perform five- fold cross validation on five datasets for model evaluation and our model surpasses other advanced methods. The code is available at https://github.com/lanbiolab/LGCDA.

Deep Imputation Bi-stochastic Graph Regularized Matrix Factorization for Clustering Single-cell RNA-sequencing Data.

By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods.