bluuebunny/crossref_metadata_2025_split huggingface dataset:
Caspofungin enhances the potency of rifampin against Gram-negative bacteria
Authors: Li Haotian , Zhu Xiaojing , Zhang Xing , Dong Changjiang 
Published: August 2024

IntroductionDeveloping antibiotic adjuvants is an effective strategy to combat antimicrobial resistance (AMR). The envelope of Gram-negative bacteria (GNB) is a barrier to prevent the entry of antibiotics, making it an attractive target for novel antibiotic and adjuvant development.Methods and ResultsIn this study, we identified Caspofungin acetate (CAS) as an antibiotic adjuvant against GNB in the repurposing screen of 3,158 FDA-approved drugs. Checkerboard assays suggested that CAS could enhance the antimicrobial activity of rifampin or colistin against various GNB strains in vitro, Moreover, Galleria mellonella larvae infection model also indicated that CAS significantly potentiated the efficacy of rifampin against multidrug-resistant Escherichia coli 72 strain in vivo. Most importantly, resistance development assay showed that CAS was less susceptible to accelerating the resistance development of drug-sensitive strain E. coli MG1655. Functional studies and RNA-seq analysis confirmed that the mechanisms by which CAS enhanced the antimicrobial activities of antibiotics were involved in permeabilizing the bacterial cell envelope, disrupting proton motive force and inhibiting bacterial biofilm formation. Additionally, it has been found that PgaC is the CAS target and enzymatic assay has confirmed the inhibition activity.DiscussionOur results illustrate the feasibility of CAS as an antibiotic adjuvant against GNB, which is an alternative strategy of anti-infection.
Management of Meige syndrome with bilateral trigeminal and facial nerves combing
Authors: Ying Tingting , Wang Haopeng , Tang Yinda , Zhao Hua , Cai Xiaomin , Shen Yiman , Wang Baimiao , Zhu Wanchun , Zhou Ping , Zhang Xin , Zhong Jun , Wang Xinjun , Fu Xudong , Zhu Jin , Zhang Weituo , Li Shiting 
Published: August 2024

ObjectiveMeige syndrome (MS) is an adult-onset segmental dystonia for which no satisfactory remedy currently exists. Our team developed a novel surgical approach called bilateral trigeminal/facial nerve combing (BTFC). This study aimed to evaluate the outcomes of patients who underwent BFTC (Clinical Trial Registry Number: ChiCTR2000033481).MethodWe assigned 22 patients with MS to undergo BTFC. The primary outcome was assessed using the movement subscale of the Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS-M) at 12 months postoperatively. The second outcome was evaluated using the Medical Outcome Study (MOS) 36-item Short Form Health Survey (SF-36), the dysfunction subscale of the Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS-D), and the sub-item scores of the BFMDRS-M. Safety outcomes included the House-Brackmann (HB) functional grading score and the visual analog scale (VAS) for facial numbness.ResultsAt the final follow-up at 12 months, the BFMDRS-M showed a mean improvement of 70.7% from baseline. Mean scores of the BFMDRS-M sub-motor (including the eyes, mouth, and speech/swallowing) improved by 65.6, 81.00, and 60%, respectively. The median score of the total BFMDRS-D score was 0.70 ± 1.17 compared with 1.86 ± 2.21 at baseline. There were no serious operative complications in this population. The quality of life of the patients significantly improved (P &lt; 0.05).ConclusionBFTC has proven to be effective in relieving the symptoms of Meige syndrome. This novel surgical approach offers a new alternative treatment for patients who have failed to respond to medications, botulinum toxin injections, and deep brain stimulation (DBS).Clinical Trial Registrationhttps://www.chictr.org.cn/bin/project/edit?pid=54567, ChiCTR2000033481.
Dominating Alzheimer's disease diagnosis with deep learning on sMRI and DTI-MD
Authors: Li Yuxia , Chen Guanqun , Wang Guoxin , Zhou Zhiyi , An Shan , Dai Shipeng , Jin Yuxin , Zhang Chao , Zhang Mingkai , Yu Feng 
Published: August 2024

BackgroundAlzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has become one of the major health concerns for the elderly. Computer-aided AD diagnosis can assist doctors in quickly and accurately determining patients' severity and affected regions.MethodsIn this paper, we propose a method called MADNet for computer-aided AD diagnosis using multimodal datasets. The method selects ResNet-10 as the backbone network, with dual-branch parallel extraction of discriminative features for AD classification. It incorporates long-range dependencies modeling using attention scores in the decision-making layer and fuses the features based on their importance across modalities. To validate the effectiveness of our proposed multimodal classification method, we construct a multimodal dataset based on the publicly available ADNI dataset and a collected XWNI dataset, which includes examples of AD, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN).ResultsOn this dataset, we conduct binary classification experiments of AD vs. CN and MCI vs. CN, and demonstrate that our proposed method outperforms other traditional single-modal deep learning models. Furthermore, this conclusion also confirms the necessity of using multimodal sMRI and DTI data for computer-aided AD diagnosis, as these two modalities complement and convey information to each other. We visualize the feature maps extracted by MADNet using Grad-CAM, generating heatmaps that guide doctors' attention to important regions in patients' sMRI, which play a crucial role in the development of AD, establishing trust between human experts and machine learning models.ConclusionWe propose a simple yet effective multimodal deep convolutional neural network model MADNet that outperforms traditional deep learning methods that use a single-modality dataset for AD diagnosis.
Phytochemical profiling and evaluation of antimicrobial activities of common culinary spices: Syzygium aromaticum (clove) and Piper nigrum (black pepper)
Authors: Zhao Kexin , Wonta Kou B. , Xia Jinquan , Zhong Fuhua , Sharma Vipasha 
Published: August 2024

BackgroundThe increasing resistance of microbial pathogens to conventional antibiotics necessitates the exploration of alternative antimicrobial agents. This study aims to evaluate the antimicrobial potential and phytochemical properties of Syzygium aromaticum (clove) and Piper nigrum (black pepper) extracts, both of which are known for their historical use in traditional medicine and culinary applications.MethodsHydroalcoholic and aqueous extracts of clove and black pepper were prepared. The antimicrobial activity of these extracts was assessed using the disk diffusion method against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Candida albicans, and Aspergillus niger. Minimum inhibitory concentration (MIC) was determined using the broth dilution method. Qualitative phytochemical screening identified the presence of key bioactive compounds, while quantitative analysis measured total phenolic and flavonoid contents. LC-HRMS/MS analysis of ethanolic extracts was performed.ResultsBoth spices extracts exhibited significant antimicrobial activity, with inhibition zones ranging from 14 to 18 mm. clove showed superior antimicrobial efficacy compared to black paper, particularly against fungi. MIC values ranged between 3 mg/mL and 6 mg/mL for both spices. Phytochemical analysis revealed higher total phenolic and flavonoid contents in clove, with hydroalcoholic extracts showing greater concentrations than aqueous extracts. HPLC quantified higher eugenol content in clove extracts and higher piperine content in black pepper extracts. The differences in bioactive compound content were statistically significant (p &lt; 0.05).ConclusionThe study confirms that both spices possess significant antimicrobial properties, attributable to their rich phytochemical composition, particularly phenolics and flavonoids. Clove exhibited slightly superior antimicrobial activity compared to black paper. These findings support the potential use of these spices as complementary antimicrobial agents. Further research should investigate their synergistic effects with conventional antibiotics and explore their applications in food preservation and alternative medicine.
Single versus multiple fraction stereotactic radiosurgery for medium-sized brain metastases (4-14 cc in volume): reducing or fractionating the radiosurgery dose?
Authors: Reinhardt Philipp , Ahmadli Uzeyir , Uysal Emre , Shrestha Binaya Kumar , Schucht Philippe , Hakim Arsany , Ermiş Ekin 
Published: August 2024

Background and purposeStereotactic radiosurgery (SRS) of brain metastases (BM) and resection cavities is a widely used and effective treatment modality. Based on target lesion size and anatomical location, single fraction SRS (SF-SRS) or multiple fraction SRS (MF-SRS) are applied. Current clinical recommendations conditionally recommend either reduced dose SF-SRS or MF-SRS for medium-sized BM (2–2.9 cm in diameter). Despite excellent local control rates, SRS carries the risk of radionecrosis (RN). The purpose of this study was to assess the 12-months local control (LC) rate and 12-months RN rate of this specific patient population.Materials and methodsThis single-center retrospective study included 54 patients with medium-sized intact BM (n=28) or resection cavities (n=30) treated with either SF-SRS or MF-SRS. Follow-up MRI was used to determine LC and RN using a modification of the “Brain Tumor Reporting and Data System” (BT-RADS) scoring system.ResultsThe 12-month LC rate following treatment of intact BM was 66.7% for SF-SRS and 60.0% for MF-SRS (p=1.000). For resection cavities, the 12-month LC rate was 92.9%% after SF-SRS and 46.2% after MF-SRS (p=0.013). For intact BM, RN rate was 17.6% for SF-SRS and 20.0% for MF-SRS (p=1.000). For resection cavities, RN rate was 28.6% for SF-SRS and 20.0% for MF-SRS (p=1.000).ConclusionPatients with intact BM showed no statistically significant differences in 12-months LC and RN rate following SF-SRS or MF-SRS. In patients with resection cavities the 12-months LC rate was significantly better following SF-SRS, with no increase in the RNFS.
Pilot study: radiomic analysis for predicting treatment response to whole-brain radiotherapy combined temozolomide in lung cancer brain metastases
Authors: Sun Yichu , Liang Fei , Yang Jing , Liu Yong , Shen Ziqiang , Zhou Chong , Xia Youyou 
Published: August 2024

ObjectiveThe objective of this study is to assess the viability of utilizing radiomics for predicting the treatment response of lung cancer brain metastases (LCBM) to whole-brain radiotherapy (WBRT) combined with temozolomide (TMZ).MethodsFifty-three patients diagnosed with LCBM and undergoing WBRT combined with TMZ were enrolled. Patients were divided into responsive and non-responsive groups based on the RANO-BM criteria. Radiomic features were extracted from contrast-enhanced the whole brain tissue CT images. Feature selection was performed using t-tests, Pearson correlation coefficients, and Least Absolute Shrinkage And Selection (LASSO) regression. Logistic regression was employed to construct the radiomics model, which was then integrated with clinical data to develop the nomogram model. Model performance was evaluated using receiver operating characteristic (ROC) curves, and clinical utility was assessed using decision curve analysis (DCA).ResultsA total of 1834 radiomic features were extracted from each patient's images, and 3 features with predictive value were selected. Both the radiomics and nomogram models exhibited satisfactory predictive performance and clinical utility, with the nomogram model demonstrating superior predictive value. The ROC analysis revealed that the AUC of the radiomics model in the training and testing sets were 0.776 and 0.767, respectively, while the AUC of the nomogram model were 0.799 and 0.833, respectively. DCA curves demonstrated that both models provided benefits to patients across various thresholds.ConclusionRadiomic-defined image biomarkers can effectively predict the treatment response of WBRT combined with TMZ in patients with LCBM, offering potential to optimize treatment decisions for this condition.
Hematological indicator-based machine learning models for preoperative prediction of lymph node metastasis in cervical cancer
Authors: Zhao Huan , Wang Yuling , Sun Yilin , Wang Yongqiang , Shi Bo , Liu Jian , Zhang Sai 
Published: August 2024

BackgroundLymph node metastasis (LNM) is an important prognostic factor for cervical cancer (CC) and determines the treatment strategy. Hematological indicators have been reported as being useful biomarkers for the prognosis of a variety of cancers. This study aimed to evaluate the feasibility of machine learning models characterized by preoperative hematological indicators to predict the LNM status of CC patients before surgery.MethodsThe clinical data of 236 patients with pathologically confirmed CC were retrospectively analyzed at the Gynecology Oncology Department of the First Affiliated Hospital of Bengbu Medical University from November 2020 to August 2022. The least absolute shrinkage and selection operator (LASSO) was used to select 21 features from 35 hematological indicators and for the construction of 6 machine learning predictive models, including Adaptive Boosting (AdaBoost), Gaussian Naive Bayes (GNB), and Logistic Regression (LR), as well as Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost). Evaluation metrics of predictive models included the area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, and F1-score.ResultsRF has the best overall predictive performance for ten-fold cross-validation in the training set. The specific performance indicators of RF were AUC (0.910, 95% confidence interval [CI]: 0.820–1.000), accuracy (0.831, 95% CI: 0.702–0.960), specificity (0.835, 95% CI: 0.708–0.962), sensitivity (0.831, 95% CI: 0.702–0.960), and F1-score (0.829, 95% CI: 0.696–0.962). RF had the highest AUC in the testing set (AUC = 0.854).ConclusionRF based on preoperative hematological indicators that are easily available in clinical practice showed superior performance in the preoperative prediction of CC LNM. However, investigations on larger external cohorts of patients are required for further validation of our findings.
Assessment of pathological grade and variants of bladder cancer with a continuous-time random-walk diffusion model
Authors: Wang Wei , Wu Jingyun , Shen Qi , Li Wei , Xue Ke , Yang Yuxin , Qiu Jianxing 
Published: August 2024

PurposeTo evaluate the efficacy of high b-value diffusion-weighted imaging (DWI) with a continuous-time random-walk (CTRW) diffusion model in determining the pathological grade and variant histology (VH) of bladder cancer (BCa).MethodsA total of 81 patients (median age, 70 years; range, 35-92 years; 18 females; 66 high grades; 30 with VH) with pathologically confirmed bladder urothelial carcinoma were retrospectively enrolled and underwent bladder MRI on a 3.0T MRI scanner. Multi-b-value DWI was performed using 11 b-values. Three CTRW model parameters were obtained: an anomalous diffusion coefficient (D) and two parameters reflecting temporal (α) and spatial (β) diffusion heterogeneity. The apparent diffusion coefficient (ADC) was calculated using b0 and b800. D, α, β, and ADC were statistically compared between high- and low-grade BCa, and between pure urothelial cancer (pUC) and VH. Comparisons were made using the Mann–Whitney U test between different pathological states. Receiver operating characteristic curve analysis was used to assess performance in differentiating the pathological states of BCa.ResultsADC, D, and α were significantly lower in high-grade BCa compared to low-grade, and in VH compared to pUC (p &lt; 0.001), while β showed no significant differences (p &gt; 0.05). The combination of D and α yielded the best performance for determining BCa grade and VH (area under the curves = 0.913, 0.811), significantly outperforming ADC (area under the curves = 0.823, 0.761).ConclusionThe CTRW model effectively discriminated pathological grades and variants in BCa, highlighting its potential as a noninvasive diagnostic tool.
Clinacanthus nutans extract lowers periodontal inflammation under high-glucose conditions via inhibiting NF-κB signaling pathway
Authors: Thongyim Saruda , Wright Thomas A. , Sattayawat Pachara , Kaewkod Thida , Baillie George S. , Tragoolpua Yingmanee , Jangsutthivorawat Siriphorn , Panya Aussara 
Published: August 2024

Periodontal disease is more prevalent in patients with diabetes, and it has a negative impact on their quality of life. Inhibiting the infection and inflammation processes that cause periodontal disease can reduce the severity of the disease and chances of serious complications. In this study, we aimed to demonstrate the effectiveness of Clinacanthus nutans extract in reducing the inflammation in gingival fibroblast cells induced by Porphyromonas gingivalis lipopolysaccharide (LPS). Stimulation with LPS under high-glucose conditions led to increased inflammation compared to low-glucose conditions. Treatment of C. nutans extract significantly reduced the expression of these pro-inflammatory cytokines and chemokines. At a concentration of 50 μg/mL, it reduced the relative expression of IL6, IL8, and CXCL10 to 0.51 ± 0.09, 0.6 ± 0.19, and 0.09 ± 0.02, respectively, compared to the non-treatment control, accompanied by a decrease in secreted protein as measured by ELISA. Additionally, application of C. nutans extract markedly suppressed the NF-κB signaling pathway by reducing the phosphorylated form of IκBα, NF-κB p65, and nuclear translocation of NF-κB, along with a decrease in COX2, a key mediator in the inflammatory pathway. Furthermore, analysis of RNA sequencing data indicated that the extract clearly reversed the gene expression changes induced by LPS. This was particularly true for the signaling mediators and inflammatory genes in response to NF-κB, JAK/STAT, and TNF signaling pathways. Our finding highlights the potential of C. nutans extract to alleviate inflammation and suggests its potential as a treatment for periodontal disease in patients with diabetes.
Comprehensive fecal metabolomics and gut microbiota study of the protective mechanism of herbal pair Polygonum hydropiper-Coptis chinensis in rats with stress-induced gastric mucosal damage
Authors: Ren Shouzhong , Ren Chenhui , Zhao Yamei , Niu Haiyan , Xie Yiqiang 
Published: August 2024

Introduction: Stress-related gastric mucosal lesions (SGMLs) are the most common complication in critical care patients. Previous studies have demonstrated that herbal pair (HP), Polygonum hydropiper-Coptis chinensis (HP P-C) has the anti-SGML effect. However, the underlying mechanism of HP P-C against SGML remains elusive. This study aimed to elucidate how HP P-C extracts exert their protective effects on SGML by examining the role of gut microbiota and metabolites.Methods: SD rats were pretreated with different doses of HP P-C extracts for 6 days, followed by inducing SGML with water-immersion restraint stress (WIRS). After a comprehensive evaluation of serum and gastric tissue indicators in rats, 16S rRNA sequencing and metabolomics analyses were conducted to assess the impact of HP P-C on the fecal microorganisms and metabolites and their correlation.Results: Animal experiment suggested that pretreatment with HP P-C effectively reduced the gastric mucosal lesions, remarkably increased superoxide dismutase (SOD) activity in SGML model rats induced by WIRS. 16S rRNA sequencing analysis showed that HP P-C altered the composition of gut microbiota by raising the abundance of Lactobacillus and Akkermansia. In addition, metabolomics data identified seventeen main differential metabolites related to WIRS-induced gastric mucosal injury, primarily involving in tyrosine metabolism and betalain biosynthesis. HP P-C was found to regulate tyrosine metabolism and betalain biosynthesis by down-regulating the tyramine, L-tyrosine and L-dopa and up -regulating the gentisic acid and dopaquinone.Conclusion: Taken together, this study indicated that HP P-C could effectively protect against WIRS-induced gastric mucosal lesions by modulating intestinal flora and metabolites.
Transcription factor ZEB1 coordinating with NuRD complex to promote oncogenesis through glycolysis in colorectal cancer
Authors: Gao Tianyang , Hao Xinhui , Zhang Jingyao , Huo Miaomiao , Hu Ting , Ma Tianyu , Yu Hefen , Teng Xu , Wang Yong , Yang Yunkai , Huang Wei , Wang Yan 
Published: August 2024

BackgroundColorectal cancer (CRC) is an aggressive primary intestinal malignancy with the third-highest incidence and second-highest mortality among all cancer types worldwide. Transcription factors (TFs) regulate cell development and differentiation owing to their ability to recognize specific DNA sequences upstream of genes. Numerous studies have demonstrated a strong correlation between TFs, the etiology of tumors, and therapeutic approaches. Here, we aimed to explore prognosis-related TFs and comprehend their carcinogenic mechanisms, thereby offering novel insights into the diagnosis and management of CRC.Materials and MethodsDifferentially expressed TFs between CRC and normal tissues were identified leveraging The Cancer Genome Atlas database, Weighted correlation network analysis and Cox regression analysis were performed to identify prognosis-related TFs. The cellular functions of hub TF zinc finger E-box binding homeobox 1 (ZEB1) were determined using by 5-ethynyl-2′-deoxyuridine and cell invasion assays in CRC cells. RNA-sequencing, Kyoto Encyclopedia of Genes and Genomes enrichment, and gene set enrichment analyses were used to identify the cellular processes in which ZEB1 participates. Immunoaffinity purification, silver staining mass spectrometry, and a chromatin immunoprecipitation assay were conducted to search for proteins that might interact with ZEB1 and the target genes they jointly regulate.ResultsThirteen central TFs related to prognosis were identified through bioinformatics analysis techniques. Among these TFs, ZEB1 emerged as the TF most closely associated with CRC, as determined through a combination of regulatory network diagrams, survival curves, and phenotype analyses. ZEB1 promotes CRC cell growth by recruiting the NuRD(MTA1) complex, and the ZEB1/NuRD(MTA1) complex transcriptionally represses glycolysis-associated tumor suppressor genes.ConclusionOur study not only identified a hub biomarker related to CRC prognosis but also revealed the specific molecular mechanisms through which ZEB1 affects cancer progression. These insights provide crucial evidence for the diagnosis of CRC and potential treatment opportunities.