Analysis of the Spectrum of Common Pathogens and the Resistance of Mycoplasma in Sialidase-Positive Bacterial Vaginosis.

BACKGROUND: The objective of this study is to understand the characteristics of the common spectrum of pathogen and the resistance of Mycoplasma in Sialidase-positive bacterial vaginosis. METHODS: The vaginal secretion specimens collected from August 2018 to October 2018 for the analysis of bacterial vaginosis (BV) were subjected to various techniques. These included routine leukorrhea examination, bacterial vaginosis sialidase testing, routine culture for common pathogens, mass spectrometry identification, and Mycoplasma resistance testing. RESULTS: A total of 238 patients with BV were identified. The cleanliness grading was mostly clean (+) and clean (2+), accounting for 38.24% and 30.67%, respectively. The bacterial vaginosis test for vaginal secretions showed leukocyte esterase positivity in 220 cases, resulting in a positivity rate of 92.44%. The spectrum of routine culture was analyzed and divided into four groups: A, B, C, and D. Group A consisted of Candidal vaginitis (13.45%); group B consisted of Gardnerella vaginalis vaginitis (32.77%); group C consisted of gram-negative bacillus vaginitis (46.22%); and group D consisted of Streptococcus agalactiae vaginitis (7.56%). The identification and antimicrobial susceptibility testing results for Mycoplasma showed a high detection rate of BV, with a positivity rate of 86.13%. There was a high sensitivity to tetracyclines for Ureaplasma urealyticum and Mycoplasma hominis, but a high resistance to macrolides and quinolones. CONCLUSIONS: Bacterial vaginosis existed in various complex forms, including Candida, Gardnerella vaginalis, Gram-negative bacillus, and Streptococcus agalactiae types. Moreover, there was an increasing trend of multi-drug resistance in Mycoplasma hominis. Therefore, it is crucial to pay attention to this condition and make accurate judgments based on the etiological characteristics and common antimicrobial susceptibility tests. This will enable the implementation of effective therapeutic interventions.

Microbiology and Clinical Outcome of Bloodstream Infections in Patients After Hematopoietic Stem Cell Transplantation.

Purpose: Patients after hematopoietic stem cell transplantation (HSCT) are often followed by bloodstream infections (BSIs). BSI is an important cause of non-relapse mortality (NRM) in HSCT patients. Methods: We conducted a retrospective cohort study of patients (aged >14 years) who underwent HSCT at our hospital from 2017 to 2021. Population characteristics, BSI microbiology, resistance to common antibiotics, and 30-day all-cause mortality were analyzed. Results: Of 3054 patients, 169 (5.5%) had BSIs after HSCT. Male, not in complete remission at transplantation and longer duration of neutropenia were risk factors for the development of BSI after HSCT. These BSIs were Gram-negative bacterial (n=123, 69.49%), Gram-positive bacterial (n=27, 15.25%), fungal (n=11, 6.36%), and polymicrobial (n=16, 9.25%). Among the Gram-negative bacteria, the proportions of isolates resistant to ceftazidime, cefepime, and piperacillin-tazobactam were similar (72.93%, 74.80%, and 77.42%, respectively). The overall drug resistance rates of amikacin and imipenem were 16.13% and 43.90%, respectively. Staphylococcus isolates were methicillin-resistant. In Enterococcus isolates, the penicillin resistance rate was 84.62%. Eleven isolates of Candida tropicalis were resistant to fluconazole and were sensitive to amphotericin B and flucytosine. The 30-day all-cause mortality rate of the 169 patients with BSIs was 8.88%. The 30-day all-cause mortality of patients with Gram-negative bacterial BSIs was 7.32%, 25.00% for polymicrobial BSIs, and 36.36% for fungal BSIs. The 30-day all-cause mortality of patients with fungal BSIs was significantly higher than that of patients with Gram-negative (P=0.0023) and Gram-positive bacteria (P=0.0023). Fungal BSI and non-Hodgkin's lymphoma (NHL) were associated with higher 30-day mortality. Conclusion: Our study reveals the microbiological characteristics and 30-day all-cause mortality in patients with bloodstream infections after HSCT. Our data provides strong support for empirical antimicrobial therapy and infection prevention strategies for patients with BSIs after HSCT.

Incidence and risk factors for suicidal ideation in a sample of Chinese patients with mixed cancer types.

PURPOSE: Suicidal ideation (SI) is often overlooked as a risk factor for people with cancer. Because it is often a precursor for suicidal behavior, it is critical to identify and address SI in a timely manner. This study investigated SI incidence and risk factors in a cohort of Chinese patients with mixed cancer types. METHODS: Data from this cross-sectional study were collected from 588 patients receiving medical therapy for tumors at Nanfang Hospital and the Integrated Hospital of Traditional Chinese Medicine at Southern Medical University. SI was measured using the Self-rating Idea of Suicide Scale (SIOSS). Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS). The Chinese version of the Demoralization Scale II (DS-II-C) was used to assess demoralization. Univariate and correlation analyses were used to identify correlative factors of SI and multiple stepwise linear regression analysis was used to characterize potential risk factors. RESULTS: SI was reported in 24.7% of participants and the SIOSS score was 14.00 (13.00, 15.00) in the SI group. Multiple linear regression results showed that demoralization, medical financial burden, cancer type, living condition, caretaker, working state, residence, gender, and marital status explained 32.1% of the SI in this cohort (F = 28.705, P < 0.001). CONCLUSION: Approximately one-quarter of cancer patients in this study reported SI influenced by both external and internal factors. Characterizing these factors can be informative for prevention and treatment efforts.

DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19.

Given that the usual process of developing a new vaccine or drug for COVID-19 demands significant time and funds, drug repositioning has emerged as a promising therapeutic strategy. We propose a method named DRPADC to predict novel drug-disease associations effectively from the original sparse drug-disease association adjacency matrix. Specifically, DRPADC processes the original association matrix with the WKNKN algorithm to reduce its sparsity. Furthermore, multiple types of similarity information are fused by a CKA-MKL algorithm. Finally, a compressed sensing algorithm is used to predict the potential drug-disease (virus) association scores. Experimental results show that DRPADC has superior performance than several competitive methods in terms of AUC values and case studies. DRPADC achieved the AUC value of 0.941, 0.955 and 0.876 in Fdataset, Cdataset and HDVD dataset, respectively. In addition, the conducted case studies of COVID-19 show that DRPADC can predict drug candidates accurately.

HBRWRLDA: predicting potential lncRNA-disease associations based on hypergraph bi-random walk with restart.

Accumulating evidence indicates that the regulation of long non-coding RNAs (lncRNAs) is closely related to a variety of diseases. Identifying meaningful lncRNA-disease associations will help to contribute to the understanding of the molecular mechanisms underlying these diseases. However, only a limited number of associations between lncRNAs and diseases have been inferred from traditional biological experiments due to the high cost and highly specialized. Therefore, computational methods are increasingly used to reduce time of biological experiments and complement biological research. In this paper, a computational method called HBRWRLDA is proposed to predict lncRNA-disease associations. First, HBRWRLDA models the relationships between multiple nodes using hypergraphs, which allows HBRWRLDA to integrate the expression similarity of lncRNAs and the semantic similarity of diseases to construct hypergraphs. Then, a bi-random walk on hypergraphs is used to predict potential lncRNA-disease associations. HBRWRLDA achieves a higher area under the curve value of 0.9551 and [Formula: see text], respectively, compared with the other five advanced methods under the framework of one-leave cross validation (LOOCV) and five-fold cross-validation (5-fold CV). In addition, the prediction effect of HBRWRLDA was confirmed case studies of three diseases: renal cell carcinoma, gastric cancer, and hepatocellular carcinoma. Case studies demonstrates the capacity of HBRWRLDA to identify potentially disease-associated lncRNAs. Overall, HBRWRLDA is excellent at predicting potential lncRNA-disease associations and could be useful in conducting further biological experiments by helping researchers identify candidates of lncRNA-disease association.

SCCPMD: Probability matrix decomposition method subject to corrected similarity constraints for inferring long non-coding RNA-disease associations.

Accumulating evidence has demonstrated various associations of long non-coding RNAs (lncRNAs) with human diseases, such as abnormal expression due to microbial influences that cause disease. Gaining a deeper understanding of lncRNA-disease associations is essential for disease diagnosis, treatment, and prevention. In recent years, many matrix decomposition methods have also been used to predict potential lncRNA-disease associations. However, these methods do not consider the use of microbe-disease association information to enrich disease similarity, and also do not make more use of similarity information in the decomposition process. To address these issues, we here propose a correction-based similarity-constrained probability matrix decomposition method (SCCPMD) to predict lncRNA-disease associations. The microbe-disease associations are first used to enrich the disease semantic similarity matrix, and then the logistic function is used to correct the lncRNA and disease similarity matrix, and then these two corrected similarity matrices are added to the probability matrix decomposition as constraints to finally predict the potential lncRNA-disease associations. The experimental results show that SCCPMD outperforms the five advanced comparison algorithms. In addition, SCCPMD demonstrated excellent prediction performance in a case study for breast cancer, lung cancer, and renal cell carcinoma, with prediction accuracy reaching 80, 100, and 100%, respectively. Therefore, SCCPMD shows excellent predictive performance in identifying unknown lncRNA-disease associations.

BGMSDDA: a bipartite graph diffusion algorithm with multiple similarity integration for drug-disease association prediction.

Drug repositioning, a method that relies on the information from the original drug-disease association matrix, aims to identify new indications for existing drugs and is expected to greatly reduce the cost and time of drug development. However, most current drug repositioning methods make use of the original drug-disease association matrix directly without preconditioning. As relatively only a few associations between drugs and diseases have been determined from actual observations, the original drug-disease association matrix used in the prediction is sparse, which affects the performance of the prediction method. A method for mining similar features of drugs and diseases is still lacking. To solve these problems, we developed a bipartite graph diffusion algorithm with multiple similarity integration for drug-disease association prediction (BGMSDDA). First, the weight K nearest known neighbors (WKNKN) algorithm was used to reconstruct the drug-disease association matrix. Secondly, an effective method was designed to extract similar characteristics of drugs and diseases based on integrating linear neighborhood similarity and Gaussian kernel similarity. Finally, bipartite graph diffusion was used to infer undiscovered drug-disease associations. After carrying out 10-fold cross-validation experiments, BGMSDDA showed excellent performance on two datasets, specifically with AUC values of 0.939 (Fdataset) and 0.954 (Cdataset), and AUPR values of 0.466 (Fdataset) and 0.565 (Cdataset). Furthermore, to evaluate the accuracy of the results of BGMSDDA, we conducted case studies on three medically used drugs selected from Fdataset and Cdataset and validated the predictive associated diseases of each drug with some databases. Based on the results obtained, BGMSDDA was demonstrated to be useful for predicting drug-disease associations.

HOPMCLDA: predicting lncRNA-disease associations based on high-order proximity and matrix completion.

In recent years, emerging evidence has shown that long noncoding RNAs (lncRNAs) have important roles in the biological processes of complex diseases. However, experiments to determine the associations between diseases and lncRNAs are time consuming and costly. Therefore, there is a need to develop effective computational methods for exploring potential lncRNA-disease associations. In this study, we present a computational prediction method based on high-order proximity and matrix completion to predict lncRNA-disease associations (HOPMCLDA). HOPMCLDA integrates explicit similarity and high-order proximity information on lncRNAs and diseases and constructs a heterogeneous disease-lncRNA network to utilize similarity information. Finally, nuclear norm regularization is carried out on the heterogeneous network for the recovery of a lncRNA-disease association matrix. By implementing leave-one-out cross validation (LOOCV) and five-fold cross validation (5-fold CV), we compare HOPMCLDA with five other methods. HOPMCLDA outperforms the other methods, with area under the receiver operating characteristic curve values of 0.8755 and 0.8353 ± 0.0045 using LOOCV and 5-fold CV, respectively. Furthermore, case studies of three human diseases (gastric cancer, osteosarcoma, and hepatocellular carcinoma) confirm the reliable predictive performance of HOPMCLDA.

Mitochondrial DNA 4977 bp deletion is a common phenomenon in hair and increases with age.

Mitochondrial DNA (mtDNA) is believed to be particularly susceptible to oxidative damage during aging, resulting in mtDNA point mutations, duplications, and deletions. Although mtDNA deletions have been reported in various human tissues, e.g., the brain, heart, and skeletal muscle, little is known about the occurrence in hair. Therefore, we screened for the presence of mtDNA 13162 bp, 10422 bp, 7663 bp, 7436 bp, 4989 bp, and 4977 bp deletions in 90 hair samples from subjects aged 5 days to 91 years by using polymerase chain reaction (PCR) and investigated the deletion load by TaqMan probe-based real-time PCR. We detected the mtDNA 4977 bp deletion in hair samples, but none of the other deletions that were screened for. The proportion of mtDNA 4977 deletion carriers was 98.3% (89/90) and the deletion loads increased from 0 to 1.436 ± 0.2086% of the total mtDNA with an exponential increase with age (r = 0.677, p < 0.05). These results suggest that mtDNA 4977 bp deletion is a common phenomenon in hair and increases with age. These findings expand our understanding of the tissue-specific distribution of mtDNA deletions.