Predicting potential microbe-disease associations based on dual branch graph convolutional network.

Studying the association between microbes and diseases not only aids in the prevention and diagnosis of diseases, but also provides crucial theoretical support for new drug development and personalized treatment. Due to the time-consuming and costly nature of laboratory-based biological tests to confirm the relationship between microbes and diseases, there is an urgent need for innovative computational frameworks to anticipate new associations between microbes and diseases. Here, we propose a novel computational approach based on a dual branch graph convolutional network (GCN) module, abbreviated as DBGCNMDA, for identifying microbe-disease associations. First, DBGCNMDA calculates the similarity matrix of diseases and microbes by integrating functional similarity and Gaussian association spectrum kernel (GAPK) similarity. Then, semantic information from different biological networks is extracted by two GCN modules from different perspectives. Finally, the scores of microbe-disease associations are predicted based on the extracted features. The main innovation of this method lies in the use of two types of information for microbe/disease similarity assessment. Additionally, we extend the disease nodes to address the issue of insufficient features due to low data dimensionality. We optimize the connectivity between the homogeneous entities using random walk with restart (RWR), and then use the optimized similarity matrix as the initial feature matrix. In terms of network understanding, we design a dual branch GCN module, namely GlobalGCN and LocalGCN, to fine-tune node representations by introducing side information, including homologous neighbour nodes. We evaluate the accuracy of the DBGCNMDA model using five-fold cross-validation (5-fold-CV) technique. The results show that the area under the receiver operating characteristic curve (AUC) and area under the precision versus recall curve (AUPR) of the DBGCNMDA model in the 5-fold-CV are 0.9559 and 0.9630, respectively. The results from the case studies using published experimental data confirm a significant number of predicted associations, indicating that DBGCNMDA is an effective tool for predicting potential microbe-disease associations.

Metabolism score and machine learning models for the prediction of esophageal squamous cell carcinoma progression.

The incomplete prediction of prognosis in esophageal squamous cell carcinoma (ESCC) patients is attributed to various therapeutic interventions and complex prognostic factors. Consequently, there is a pressing demand for enhanced predictive biomarkers that can facilitate clinical management and treatment decisions. This study recruited 491 ESCC patients who underwent surgical treatment at Huashan Hospital, Fudan University. We incorporated 14 blood metabolic indicators and identified independent prognostic indicators for overall survival through univariate and multivariate analyses. Subsequently, a metabolism score formula was established based on the biochemical markers. We constructed a nomogram and machine learning models utilizing the metabolism score and clinically significant prognostic features, followed by an evaluation of their predictive accuracy and performance. We identified alkaline phosphatase, free fatty acids, homocysteine, lactate dehydrogenase, and triglycerides as independent prognostic indicators for ESCC. Subsequently, based on these five indicators, we established a metabolism score that serves as an independent prognostic factor in ESCC patients. By utilizing this metabolism score in conjunction with clinical features, a nomogram can precisely predict the prognosis of ESCC patients, achieving an area under the curve (AUC) of 0.89. The random forest (RF) model showed superior predictive ability (AUC = 0.90, accuracy = 86%, Matthews correlation coefficient = 0.55). Finally, we used an RF model with optimal performance to establish an online predictive tool. The metabolism score developed in this study serves as an independent prognostic indicator for ESCC patients.

Pre-pubertal sublingual immunotherapy is more effective than immunotherapy during puberty in allergic rhinitis and asthma.

BACKGROUND: To evaluate the clinical efficacy of sublingual-specific immunotherapy (SLIT) and pulmonary function in children with allergic rhinitis and asthma before and after puberty. METHODS: This retrospective analysis included 136 patients aged 4-18 years with allergic asthma and rhinitis who received two years of SLIT treatment. Patients were divided into two groups based on age: the prepubertal group (4-10 years old) and the pubertal group (11-18 years old). After half a year, one year, and two years of SLIT, the total nasal symptom score (TNSS), total rhinitis medication score (TRMS), daytime asthma symptom score (DASS), nighttime asthma symptom score (NASS), total asthma medication score (TAMS), asthma control test (ACT), and peak expiratory flow rate (PEF%) were evaluated and compared with the baseline before treatment. RESULTS: In both groups, TNSS, TRMS, DASS, NASS, TAMS, ACT, and PEF% improved significantly after half a year, one year, and two years of SLIT treatment. After half a year of treatment, prepubertal patients showed better therapy for TNSS, DASS, NASS, and TAMS compared to the pubertal group. The TAMS of the pubertal group was higher than that of the prepubertal group after one year of treatment. Finally, the PEF% showed better therapy compared to the pubertal group. CONCLUSION: SLIT treatment with Dermatophagoides farinae drops can effectively control the symptoms of rhinitis and asthma in children with allergic rhinitis and asthma before and after puberty, reduce the use of symptomatic drugs, significantly improve the pulmonary function of patients, and have better effects on asthma in prepubertal children than in adolescents.

Antibacterial activity of closantel against methicillin-resistant Staphylococcus aureus and itsbiofilm. / æ°¯æ°°ç¢˜æŸ³èƒºå¯¹è€ç”²æ°§è¥¿æž—é‡‘é»„è‰²è‘¡è„çƒèŒåŠå ¶ç”Ÿç‰©è†œçš„æŠ—èŒæ´»æ€§.

OBJECTIVES: The antimicrobial resistance of Staphylococcus aureus (S. aureus) has become a challenge in the treatment of infectious diseases. It is of great clinical value to discovery effective antimicrobial agents against multi-drug resistant S. aureus and its biofilms. This study aims to explore the antibacterial activity of the antiparasitic drug closantel against methicillin-resistant S. aureus and its biofilms through drug repurposing. METHODS: The sensitivity of S. aureus to closantel was assessed using microbroth dilution and disk diffusion methods. The bacteriostatic and bactericidal activities of closantel were determined by time-kill curves and colony count. Scanning electron microscopy combined with SYTOX Green and DiSC3(5) fluorescence probes were used to study the bactericidal mechanism of closantel. The influence of resistance was assessed by continuous exposure to sub-inhibitory concentrations of closantel. The anti-biofilm activity was evaluated using 96-well plates and crystal violet staining, and cytotoxicity was measured using the CCK-8 assay. RESULTS: The minimal inhibitory concentration (MIC) of closantel for both methicillin-sensitive and methicillin-resistant S. aureus ranged from 0.125 to 1.000 µg/mL. Disk diffusion tests showed that 80 µg of closantel created an inhibition zone, which increased in diameter with higher drug amounts. Sub-inhibitory concentrations (0.031 µg/mL) of closantel significantly inhibited S. aureus proliferation, reducing bacterial turbidity from 0.26±0.00 to 0.11±0.01 (t=16.06, P<0.001), with stronger inhibition at higher concentrations. Closantel at 0.25×MIC inhibited S. aureus proliferation for 12 hours, while 1×MIC inhibited it for over 24 hours, with the number of viable bacteria decreasing as the drug concentration increased. Mechanistic studies indicated that closantel effectively disrupted the integrity of S. aureus cell membranes, significantly increasing SYTOX Green and DiSC3(5) fluorescence intensity. Even after 25 days of continuous exposure to sub-inhibitory concentrations of closantel, no resistance developed. Closantel at 0.0625 µg/mL significantly inhibited biofilm formation, reducing it from 1.29±0.16 to 0.62±0.04 (t=11.62, P<0.001), showing a clear dose-dependent effect. Closantel at 2 µg/mL also significantly eradicated established biofilms, reducing biofilm mass from 1.62±0.34 to 0.51±0.39 (t=4.84, P<0.01). Additionally, closantel exhibited extremely low cytotoxicity, with half-maximal lethal concentrations for HepG2 liver cancer cells and normal LO2 liver cells both exceeding 64 µg/mL. CONCLUSIONS: Closantel exhibits strong antibacterial activity against S. aureus and its biofilm with low cytotoxicity against human cells, making it a promising candidate for new therapeutic strategies against S. aureus-related infections.

ESRRG-PKM2 axis reprograms metabolism to suppress esophageal squamous carcinoma progression and enhance anti-PD-1 therapy efficacy.

BACKGROUND: Glycolysis under normoxic conditions, known as the Warburg effect, confers a selective advantage for the survival and proliferation of many tumors. In this study, we investigated the role of estrogen-related receptor gamma (ESRRG) in metabolic reprogramming in esophageal squamous cell carcinoma (ESCC). METHODS: Bioinformatics analysis indicated that ESRRG expression was decreased in ESCC tissue and associated with poor clinical outcomes. We also examined the effects of altered ESRRG expression on the proliferation and metabolic reprogramming of ESCC cells. We explored the impact of ESRRG on Pyruvate kinase M2 (PKM2) expression and malignant behavior in ESCC. RESULTS: Our study revealed the inhibitory effects of ESRRG on the growth, tumorigenesis, and glycolysis activity of ESCC cells, which were mediated by the downregulation of PKM2 expression. We further demonstrated that ESRRG directly interacts with the PKM2 promoter to inhibit its activity in ESCC. Notably, the ESRRG-specific agonist, DY131, inhibited ESCC cell proliferation and glycolysis activity by modulating genes in the glycolysis pathway. Moreover, we verified that DY131 exhibits enhanced activity as an immune checkpoint inhibitor, considering the significance of the ESRRG-PKM2 axis in the lactate regulation of ESCC cells. CONCLUSION: Our findings provide novel insights into the role of ESRRG-PKM2 signaling in regulating ESCC cell metabolism and immune checkpoint regulation. Additionally, we suggest that DY131 holds promise as a promising therapeutic agent for ESCC treatment.

Renal tubular epithelial cell necroptosis promotes tubulointerstitial fibrosis in patients with chronic kidney disease.

Renal fibrosis, a common pathological manifestation of virtually all types of chronic kidney disease (CKD), ultimately predisposes patients to end-stage renal disease. However, there is no effective therapy for renal fibrosis. Our earlier studies proved that RIP3-mediated necroptosis might be an important mode of renal tubular cell death in rats with chronic renal injury. Under transmission electron microscopy (TEM), we found morphological changes in the necrosis of human renal tissue, and the percentage of necrotic cells increased significantly in patients with stages 2 and 3a CKD. Immunofluorescence analyses showed that the percentages of TUNEL+ /RIP3+ double-positive and TUNEL+ /MLKL+ double-positive tubular epithelial cells in renal tubules of patients with stages 2 and 3a CKD were significantly increased compared to those in control patients without renal disease. Immunohistochemistry analyses of renal biopsy specimens from patients with CKD revealed RIP3, MLKL, and p-MLKL upregulation in patients with stages 2 and 3a CKD, suggesting that necroptosis of renal tubular epithelial cells in CKD patients occurs, and the peak of necroptosis was in stages 2 and 3a CKD. We showed that profibrotic factor proteins (TGF-ß1, Smad2 and Smad3) and fibroblast activation markers (α-SMA and Vimentin) were specifically upregulated in stage 2 and 3a CKD patients. In addition, Pearson correlation analysis showed that the percentage of necroptotic renal tubular epithelial cells was positively correlated with TGF-ß1 and collagen-I. We also showed that RIP1/3 or MLKL inhibitors decreased the expression of RIP3, MLKL, TGF-ß1, and Smad3 in HK-2 cells treated with TNF-α. FGF-2, α-SMA, Vimentin and FN were overexpressed in the hRIFs cultured with the supernatant of necroptotic HK-2 cells, whereas necroptosis blockers (Nec-1s, GSK'872 and NSA) and TGF-ß1/Smad3 pathway antagonists (LY364947 and SIS3) reduced FGF-2, α-SMA, Vimentin and FN levels. Collectively, necroptosis of renal tubular epithelial cells in CKD patients occurs, and the peak of necroptosis was in stages 2 and 3a CKD. Renal tubular epithelial cell necroptosis mediates renal tubulointerstitial fibrosis in patients with chronic kidney disease, which is related to the TGF-ß1/Smad3 signaling pathway.

The characteristics and clinical relevance of tumor fusion burden in non-EBV (+) gastric cancer with MSS.

BACKGROUND: Next-generation sequencing (NGS) is maturely applied for gene fusion detection. Although tumor fusion burden (TFB) has been identified as an immune marker for cancer, the relationship between these fusions and the immunogenicity and molecular characteristics of gastric cancer (GC) patients remains unclear. GCs have different clinical significance depending on their subtypes, and thus, this study aimed to investigate the characteristics and clinical relevance of TFB in non-Epstein-Barr-virus-positive (EBV+) GC with microsatellite stability (MSS). METHODS: A total of 319 GC patients from The Cancer Genome Atlas stomach adenocarcinoma (TCGA-STAD) and a cohort of 45-case from ENA (PRJEB25780) were included. The cohort characteristics and distribution of TFB among the patients were analyzed. Additionally, the correlations of TFB with mutation characteristics, pathway differences, relative abundance of immune cells, and prognosis were examined in the TCGA-STAD cohort of MSS and non-EBV (+) patients. RESULTS: We observed that in the MSS and non-EBV (+) cohort, the TFB-low group exhibited significantly lower gene mutation frequency, gene copy number, loss of heterozygosity score, and tumor mutation burden than in the TFB-high group. Additionally, the TFB-low group exhibited a higher abundance of immune cells. Furthermore, the immune gene signatures were significantly upregulated in the TFB-low group, 2-year disease-specific survival was markedly increased in the TFB-low group compared with to the TFB-high group. The rates of TFB-low cases were significantly higher TFB-than high cases in durable clinical benefit (DCB) and response groups with pembrolizumab treatment. Low TFB may serve as a predictor of GC prognosis, and the TFB-low group exhibits higher immunogenicity. CONCLUSION: In conclusion, this study reveals that the TFB-based classification of GC patient may be instructive for individualized immunotherapy regimens.

An Exploratory Study of Medical Journal's Twitter Use: Metadata, Networks, and Content Analyses.

BACKGROUND: An increasing number of medical journals are using social media to promote themselves and communicate with their readers. However, little is known about how medical journals use Twitter and what their social media management strategies are. OBJECTIVE: This study aimed to understand how medical journals use Twitter from a global standpoint. We conducted a broad, in-depth analysis of all the available Twitter accounts of medical journals indexed by major indexing services, with a particular focus on their social networks and content. METHODS: The Twitter profiles and metadata of medical journals were analyzed along with the social networks on their Twitter accounts. RESULTS: The results showed that overall, publishers used different strategies regarding Twitter adoption, Twitter use patterns, and their subsequent decisions. The following specific findings were noted: journals with Twitter accounts had a significantly higher number of publications and a greater impact than their counterparts; subscription journals had a slightly higher Twitter adoption rate (2%) than open access journals; journals with higher impact had more followers; and prestigious journals rarely followed other lesser-known journals on social media. In addition, an in-depth analysis of 2000 randomly selected tweets from 4 prestigious journals revealed that The Lancet had dedicated considerable effort to communicating with people about health information and fulfilling its social responsibility by organizing committees and activities to engage with a broad range of health-related issues; The New England Journal of Medicine and the Journal of the American Medical Association focused on promoting research articles and attempting to maximize the visibility of their research articles; and the British Medical Journal provided copious amounts of health information and discussed various health-related social problems to increase social awareness of the field of medicine. CONCLUSIONS: Our study used various perspectives to investigate how medical journals use Twitter and explored the Twitter management strategies of 4 of the most prestigious journals. Our study provides a detailed understanding of medical journals' use of Twitter from various perspectives and can help publishers, journals, and researchers to better use Twitter for their respective purposes.

Ixazomib Induces Apoptosis and Suppresses Proliferation in Esophageal Squamous Cell Carcinoma through Activation of the c-Myc/NOXA Pathway.

Esophageal squamous cell carcinoma (ESCC) is one of the major subtypes of esophageal cancer. More than half of the patients with ESCC in the world are in China, and the 5-year survival rate is less than 10%. As a new oral proteasome inhibitor, ixazomib has shown strong therapeutic effect in many solid tumors. In this study, we aimed to investigate the effects of ixazomib on the proliferation inhibition and apoptosis of ESCC cells. We used four human ESCC cell lines, cell viability assay, cell cycle and apoptosis assay, reverse-transcription polymerase chain reaction (RT-PCR), Western blot, immunohistochemistry, and ESCC xenografts model to clarify the roles of the therapeutic effect and mechanism of ixazomib in ESCC. Ixazomib significantly inhibited the proliferation and induced apoptosis in ESCC cells. RT-PCR results showed that the expressions of endoplasmic reticulum stress-related gene phorbol-12-myristate-13-acetate-induced protein 1 (NOXA) and MYC proto-oncogene (c-Myc) significantly increase after treatment with ixazomib in ESCC cells. When we knocked down the NOXA and c-Myc by small interfering RNA, the therapeutic effect of ixazomib markedly decreased, which confirmed that c-Myc/NOXA pathway played a key role in the treatment of ESCC with ixazomib. In vivo, the xenograft ESCC model mice were given 10 mg/kg of ixazomib every other day for 30 days. The results showed that the tumor size in the treatment group was significantly smaller than the control group. These results suggested that ixazomib is known to suppress proliferation and induce apoptosis in ESCC cell lines, and this effect was likely mediated by increased activation of the c-Myc/NOXA signaling pathways. SIGNIFICANCE STATEMENT: Esophageal squamous cell carcinoma (ESCC) is the common worldwide malignant tumor, but conventional chemotherapeutics suffer from a number of limitations. In this study, the results suggested that ixazomib suppresses proliferation and induces apoptosis in ESCC cell lines. Therefore, ixazomib may be a potential new strategy for ESCC therapy.

Network embedding in biomedical data science.

Owning to the rapid development of computer technologies, an increasing number of relational data have been emerging in modern biomedical research. Many network-based learning methods have been proposed to perform analysis on such data, which provide people a deep understanding of topology and knowledge behind the biomedical networks and benefit a lot of applications for human healthcare. However, most network-based methods suffer from high computational and space cost. There remain challenges on handling high dimensionality and sparsity of the biomedical networks. The latest advances in network embedding technologies provide new effective paradigms to solve the network analysis problem. It converts network into a low-dimensional space while maximally preserves structural properties. In this way, downstream tasks such as link prediction and node classification can be done by traditional machine learning methods. In this survey, we conduct a comprehensive review of the literature on applying network embedding to advance the biomedical domain. We first briefly introduce the widely used network embedding models. After that, we carefully discuss how the network embedding approaches were performed on biomedical networks as well as how they accelerated the downstream tasks in biomedical science. Finally, we discuss challenges the existing network embedding applications in biomedical domains are faced with and suggest several promising future directions for a better improvement in human healthcare.