Repurposing of SARS-CoV-2 compounds against Marburg Virus using MD simulation, mm/GBSA, PCA analysis, and free energy landscape.

The significant mortality rate associated with Marburg virus infection made it the greatest hazard among infectious diseases. Drug repurposing using in silico methods has been crucial in identifying potential compounds that could prevent viral replication by targeting the virus's primary proteins. This study aimed at repurposing the drugs of SARS-CoV-2 for identifying potential candidates against the matrix protein VP40 of the Marburg virus. Virtual screening was performed where the control compound, Nilotinib, showed a binding score of -9.99 kcal/mol. Based on binding scores, hit compounds 9549298, 11960895, 44545852, 51039094, and 89670174 were selected that had a lower binding score than the control. Subsequent molecular dynamics (MD) simulation revealed that compound 9549298 consistently formed a hydrogen bond with the residue Gln290. This was observed both in molecular docking and MD simulation poses, indicating a strong and significant interaction with the protein. 11960895 had the most stable and consistent RMSD pattern exhibited in 100 ns simulation, while 9549298 had the most identical RMSD plot compared to the control molecule. MM/PBSA analysis showed that the binding free energy (ΔG) of 9549298 and 11960895 was lower than the control, with -30.84 and -38.86 kcal/mol, respectively. It was observed by the PCA (principal component analysis) and FEL (free energy landscape) analysis that compounds 9549298 and 11960895 had lesser conformational variation. Overall, this study proposed 9549298 and 11960895 as potential binders of VP40 MARV that can cause its inhibition, however it inherently lacks experimental validation. Furthermore, the study proposes in-vitro experiments as the next step to validate these computational findings, offering a practical approach to further explore these compounds' potential as antiviral agents.Communicated by Ramaswamy H. Sarma.

CRISPR/Cas9 as a therapeutic tool for triple negative breast cancer: from bench to clinics.

Clustered regularly interspaced short palindromic repeats (CRISPR) is a third-generation genome editing method that has revolutionized the world with its high throughput results. It has been used in the treatment of various biological diseases and infections. Various bacteria and other prokaryotes such as archaea also have CRISPR/Cas9 systems to guard themselves against bacteriophage. Reportedly, CRISPR/Cas9-based strategy may inhibit the growth and development of triple-negative breast cancer (TNBC) via targeting the potentially altered resistance genes, transcription, and epigenetic regulation. These therapeutic activities could help with the complex issues such as drug resistance which is observed even in TNBC. Currently, various methods have been utilized for the delivery of CRISPR/Cas9 into the targeted cell such as physical (microinjection, electroporation, and hydrodynamic mode), viral (adeno-associated virus and lentivirus), and non-viral (liposomes and lipid nano-particles). Although different models have been developed to investigate the molecular causes of TNBC, but the lack of sensitive and targeted delivery methods for in-vivo genome editing tools limits their clinical application. Therefore, based on the available evidences, this review comprehensively highlighted the advancement, challenges limitations, and prospects of CRISPR/Cas9 for the treatment of TNBC. We also underscored how integrating artificial intelligence and machine learning could improve CRISPR/Cas9 strategies in TNBC therapy.

Identification of small molecule inhibitors of RAD52 for breast cancer therapy: in silico approach.

The breast cancer susceptibility gene 1/2 (BRCA1/2) are the key regulators in maintaining the genomic integrity and mutations in these genes have been associated with development of breast and ovarian cancers. Also, synthetic lethality has been shown in BRCA1/2 deficient cancers, when the RAD52 gene is silenced by shRNA or small molecules aptamers, suggesting a role for RAD52 in the breast cancers pathogenesis. Thus, to find the potential inhibitors of RAD52, a collection of 21,000 compounds from the ChemBridge screening library was screened to conduct molecular docking and molecular dynamics simulation (MD) against RAD52. Further, the results were validated by a density functional theory (DFT) analysis and using post-dynamics free energy calculations. Out of all screened molecules, the docking study revealed five compounds were found to have promising activities against RAD52. Moreover, the catalytic amino acid residues of RAD52 developed stable contacts with compound 8758 and 10593, as anticipated by DFT calculation, MD simulation, and post dynamics MM-GBSA energy calculation. It appears that compound 8758 is the best inhibitor against RAD52 followed by 10593 compared to the other top hits, in terms of the HOMO orbital energy (-1.0966 eV and -1.2136 eV) from DFT and the post dynamics binding free energy calculation (-54.71 and -52.43 Kcal/mol). Furthermore, a drug-like properties of lead molecules (8758 and 10593) were also seen via ADMET analysis. Based on our computational analysis, we hypothesize that a small molecule 8758 and 10593 possess the therapeutic potential in the management for breast cancer patients with a BRCA mutation via targeting RAD52.Communicated by Ramaswamy H. Sarma.

Transcriptomic Profiling Unravels Novel Deregulated Gene Signatures Associated with Acute Myocardial Infarction: A Bioinformatics Approach.

Acute myocardial infarction (AMI) is a severe disease with elevated morbidity and mortality rate worldwide. This is attributed to great losses of cardiomyocytes, which can trigger the alteration of gene expression patterns. Although several attempts have been made to assess the AMI biomarkers, to date their role in rescuing myocardial injury remains unclear. Therefore, the current study investigated three independent microarray-based gene expression datasets from AMI patients (n = 85) and their age-sex-matched healthy controls (n = 70), to identify novel gene signatures that might be involved in cardioprotection. The differentially expressed genes (DEGs) were analyzed using 'GEO2R', and weighted gene correlation network analysis (WGCNA) was performed to identify biomarkers/modules. We found 91 DEGs, of which the number of upregulated and downregulated genes were 22 and 5, respectively. Specifically, we found that the deregulated genes such as ADOR-A3, BMP6, VPS8, and GPx3, may be associated with AMI. WGCNA revealed four highly preserved modules among all datasets. The 'Enrichr' unveiled the presence of miR-660 and STAT1, which is known to affect AMI severity. Conclusively, these genes and miRNA might play a crucial role the rescue of cardiomyocytes from severe damage, which could be helpful in developing appropriate therapeutic strategies for the management of AMI.

Identification of Patients with Potential Atrial Fibrillation during Sinus Rhythm Using Isolated P Wave Characteristics from 12-Lead ECGs.

Atrial fibrillation (AF) is largely underdiagnosed. Previous studies using deep neural networks with large datasets have shown that screening AF with a 12-lead electrocardiogram (ECG) during sinus rhythm (SR) is possible. However, the poor availability of these trained models and the small size of the retrievable datasets limit its reproducibility. This study proposes an approach to generate explainable features for detecting AF during SR with limited data. We collected 94,224 12-lead ECGs from 64,196 patients from Taipei Medical University Hospital. We selected ECGs during SR from 213 patients before AF diagnosis and randomly selected 247 age-matched participants without AF records as the controls. We developed a signal-processing technique, MA-UPEMD, to isolate P waves, and quantified the spatial and temporal features using principal component analysis and inter-lead relationships. By combining these features, the machine learning models yielded AUC of 0.64. We showed that, even with this limited dataset, the P wave, representing atrial electrical activity, is depicted by our proposed approach. The extracted features performed better than the bandpass filter-extracted P waves and deep neural network model. We provided a physiologically explainable and reproducible approach for classifying patients with AF during SR.

Determining Carina and Clavicular Distance-Dependent Positioning of Endotracheal Tube in Critically Ill Patients: An Artificial Intelligence-Based Approach.

Early and accurate prediction of endotracheal tube (ETT) location is pivotal for critically ill patients. Automatic and timely detection of faulty ETT locations from chest X-ray images may avert patients' morbidity and mortality. Therefore, we designed convolutional neural network (CNN)-based algorithms to evaluate ETT position appropriateness relative to four detected key points, including tracheal tube end, carina, and left/right clavicular heads on chest radiographs. We estimated distances from the tube end to tracheal carina and the midpoint of clavicular heads. A DenseNet121 encoder transformed images into embedding features, and a CNN-based decoder generated the probability distributions. Based on four sets of tube-to-carina distance-dependent parameters (i.e., (i) 30-70 mm, (ii) 30-60 mm, (iii) 20-60 mm, and (iv) 20-55 mm), corresponding models were generated, and their accuracy was evaluated through the predicted L1 distance to ground-truth coordinates. Based on tube-to-carina and tube-to-clavicle distances, the highest sensitivity, and specificity of 92.85% and 84.62% respectively, were revealed for 20-55 mm. This implies that tube-to-carina distance between 20 and 55 mm is optimal for an AI-based key point appropriateness detection system and is empirically comparable to physicians' consensus.

Molecular mechanisms of developmental pathways in neurological disorders: a pharmacological and therapeutic review.

Developmental signalling pathways such as Wnt/ß-catenin, Notch and Sonic hedgehog play a central role in nearly all the stages of neuronal development. The term 'embryonic' might appear to be a misnomer to several people because these pathways are functional during the early stages of embryonic development and adulthood, albeit to a certain degree. Therefore, any aberration in these pathways or their associated components may contribute towards a detrimental outcome in the form of neurological disorders such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis and stroke. In the last decade, researchers have extensively studied these pathways to decipher disease-related interactions, which can be used as therapeutic targets to improve outcomes in patients with neurological abnormalities. However, a lot remains to be understood in this domain. Nevertheless, there is strong evidence supporting the fact that embryonic signalling is indeed a crucial mechanism as is manifested by its role in driving memory loss, motor impairments and many other processes after brain trauma. In this review, we explore the key roles of three embryonic pathways in modulating a range of homeostatic processes such as maintaining blood-brain barrier integrity, mitochondrial dynamics and neuroinflammation. In addition, we extensively investigated the effect of these pathways in driving the pathophysiology of a range of disorders such as Alzheimer's, Parkinson's and diabetic neuropathy. The concluding section of the review is dedicated to neurotherapeutics, wherein we identify and list a range of biological molecules and compounds that have shown enormous potential in improving prognosis in patients with these disorders.

Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients.

Survival prediction is highly valued in end-of-life care clinical practice, and patient performance status evaluation stands as a predominant component in survival prognostication. While current performance status evaluation tools are limited to their subjective nature, the advent of wearable technology enables continual recordings of patients' activity and has the potential to measure performance status objectively. We hypothesize that wristband actigraphy monitoring devices can predict in-hospital death of end-stage cancer patients during the time of their hospital admissions. The objective of this study was to train and validate a long short-term memory (LSTM) deep-learning prediction model based on activity data of wearable actigraphy devices. The study recruited 60 end-stage cancer patients in a hospice care unit, with 28 deaths and 32 discharged in stable condition at the end of their hospital stay. The standard Karnofsky Performance Status score had an overall prognostic accuracy of 0.83. The LSTM prediction model based on patients' continual actigraphy monitoring had an overall prognostic accuracy of 0.83. Furthermore, the model performance improved with longer input data length up to 48 h. In conclusion, our research suggests the potential feasibility of wristband actigraphy to predict end-of-life admission outcomes in palliative care for end-stage cancer patients. Clinical Trial Registration: The study protocol was registered on ClinicalTrials.gov (ID: NCT04883879).

Genome-Wide Scanning of Potential Hotspots for Adenosine Methylation: A Potential Path to Neuronal Development.

Methylation of adenosines at N6 position (m6A) is the most frequent internal modification in mRNAs of the human genome and attributable to diverse roles in physiological development, and pathophysiological processes. However, studies on the role of m6A in neuronal development are sparse and not well-documented. The m6A detection remains challenging due to its inconsistent pattern and less sensitivity by the current detection techniques. Therefore, we applied a sliding window technique to identify the consensus site (5'-GGACT-3') n ≥ 2 and annotated all m6A hotspots in the human genome. Over 6.78 × 107 hotspots were identified and 96.4% were found to be located in the non-coding regions, suggesting that methylation occurs before splicing. Several genes, RPS6K, NRP1, NRXN, EGFR, YTHDF2, have been involved in various stages of neuron development and their functioning. However, the contribution of m6A in these genes needs further validation in the experimental model. Thus, the present study elaborates the location of m6A in the human genome and its function in neuron physiology.

Reenvisioning Traditional to Regenerative Therapeutic Advances in Managing Nonalcoholic Fatty Liver Disease in Diabetes Mellitus.

Reports indicate the increasing prevalence of liver disorders in diabetes mellitus (DM) patients. Clinically, it has also been revealed that the existence of nonalcoholic fatty liver disease (NAFLD) enhances the incidence of type 2 diabetes mellitus (T2DM), while T2DM exacerbates NAFLD to extremely severe forms of steatohepatitis, cirrhosis, and hepatocellular carcinoma. This implies the coexistence and bidirectional nature of NAFLD and T2DM, which function synergistically to drive adverse consequences in clinical practice. For treatment of such comorbid state, though the existing practices such as lifestyle management, traditional Chinese medicines (TCM), and pharmaceuticals have offered somewhat relief, the debate continues about the optimal therapeutic impacts. Recent developments in the field of tissue engineering have led to a renewed interest in novel biomaterial alternatives such as stem cells. This might be attributable to their differentiation potential towards hepatic and pancreatic lineage. These cellular therapies could be further complemented by platelet-derived biomaterials, TCM formulations, or any specific drug. Based on these abovementioned approaches, we aimed to comprehensively analyze various preclinical and clinical studies from traditional to regenerative therapeutic approaches in managing concomitant NAFLD and T2DM.

Using Transfer Learning Method to Develop an Artificial Intelligence Assisted Triaging for Endotracheal Tube Position on Chest X-ray.

Endotracheal tubes (ETTs) provide a vital connection between the ventilator and patient; however, improper placement can hinder ventilation efficiency or injure the patient. Chest X-ray (CXR) is the most common approach to confirming ETT placement; however, technicians require considerable expertise in the interpretation of CXRs, and formal reports are often delayed. In this study, we developed an artificial intelligence-based triage system to enable the automated assessment of ETT placement in CXRs. Three intensivists performed a review of 4293 CXRs obtained from 2568 ICU patients. The CXRs were labeled "CORRECT" or "INCORRECT" in accordance with ETT placement. A region of interest (ROI) was also cropped out, including the bilateral head of the clavicle, the carina, and the tip of the ETT. Transfer learning was used to train four pre-trained models (VGG16, INCEPTION_V3, RESNET, and DENSENET169) and two models developed in the current study (VGG16_Tensor Projection Layer and CNN_Tensor Projection Layer) with the aim of differentiating the placement of ETTs. Only VGG16 based on ROI images presented acceptable performance (AUROC = 92%, F1 score = 0.87). The results obtained in this study demonstrate the feasibility of using the transfer learning method in the development of AI models by which to assess the placement of ETTs in CXRs.

Extracellular Vesicle-Based Therapy for COVID-19: Promises, Challenges and Future Prospects.

The COVID-19 pandemic has become a serious concern and has negatively impacted public health and the economy. It primarily targets the lungs, causing acute respiratory distress syndrome (ARDS); however, it may also lead to multiple organ failure (MOF) and enhanced mortality rates. Hence, there is an urgent need to develop potential effective therapeutic strategies for COVID-19 patients. Extracellular vesicles (EVs) are released from various types of cells that participate in intercellular communication to maintain physiological and pathological processes. EVs derived from various cellular origins have revealed suppressive effects on the cytokine storm during systemic hyper-inflammatory states of severe COVID-19, leading to enhanced alveolar fluid clearance, promoted epithelial and endothelial recovery, and cell proliferation. Being the smallest subclass of EVs, exosomes offer striking characteristics such as cell targeting, being nano-carriers for drug delivery, high biocompatibility, safety, and low-immunogenicity, thus rendering them a potential cell-free therapeutic candidate against the pathogeneses of various diseases. Due to these properties, numerous studies and clinical trials have been performed to assess their safety and therapeutic efficacy against COVID-19. Hence, in this review, we have comprehensively described current updates on progress and challenges for EVs as a potential therapeutic agent for the management of COVID-19.

Understanding the Future Prospects of Synergizing Minimally Invasive Transforaminal Lumbar Interbody Fusion Surgery with Ceramics and Regenerative Cellular Therapies.

Transforaminal lumber interbody fusion (TLIF) is the last resort to address the lumber degenerative disorders such as spondylolisthesis, causing lower back pain. The current surgical intervention for these abnormalities includes open TLIF. However, in recent years, minimally invasive TLIF (MIS-TLIF) has gained a high momentum, as it could minimize the risk of infection, blood loss, and post-operative complications pertaining to fusion surgery. Further advancement in visualizing and guiding techniques along with grafting cage and materials are continuously improving the safety and efficacy of MIS-TLIF. These assistive techniques are also playing a crucial role to increase and improve the learning curve of surgeons. However, achieving an appropriate output through TLIF still remains a challenge, which might be synergized through 3D-printing and tissue engineering-based regenerative therapy. Owing to their differentiation potential, biomaterials such as stem/progenitor cells may contribute to restructuring lost or damaged tissues during MIS-TLIF, and this therapeutic efficacy could be further supplemented by platelet-derived biomaterials, leading to improved clinical outcomes. Thus, based on the above-mentioned strategies, we have comprehensively summarized recent developments in MIS-TLIF and its possible combinatorial regenerative therapies for rapid and long-term relief.

The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit.

BACKGROUND: Severe sepsis and septic shock are still the leading causes of death in Intensive Care Units (ICUs), and timely diagnosis is crucial for treatment outcomes. The progression of electronic medical records (EMR) offers the possibility of storing a large quantity of clinical data that can facilitate the development of artificial intelligence (AI) in medicine. However, several difficulties, such as poor structure and heterogenicity of the raw EMR data, are encountered when introducing AI with ICU data. Labor-intensive work, including manual data entry, personal medical records sorting, and laboratory results interpretation may hinder the progress of AI. In this article, we introduce the developing of an AI algorithm designed for sepsis diagnosis using pre-selected features; and compare the performance of the AI algorithm with SOFA score based diagnostic method. MATERIALS AND METHODS: This is a prospective open-label cohort study. A specialized EMR, named TED_ICU, was implemented for continuous data recording. One hundred six clinical features relevant to sepsis diagnosis were selected prospectively. A labeling work to allocate SEPSIS or NON_SEPSIS status for each ICU patient was performed by the in-charge intensivist according to SEPSIS-3 criteria, along with the automatic recording of selected features every day by TED_ICU. Afterward, we use de-identified data to develop the AI algorithm. Several machine learning methods were evaluated using 5-fold cross-validation, and XGBoost, a decision-tree based algorithm was adopted for our AI algorithm development due to best performance. RESULTS: The study was conducted between August 2018 and December 2018 for the first stage of analysis. We collected 1588 instances, including 444 SEPSIS and 1144 NON-SEPSIS, from 434 patients. The 434 patients included 259 (59.6%) male patients and 175 female patients. The mean age was 67.6-year-old, and the mean APACHE II score was 13.8. The SEPSIS cohort had a higher SOFA score and increased use of organ support treatment. The AI algorithm was developed with a shuffle method using 80% of the instances for training and 20% for testing. The established AI algorithm achieved the following: accuracy = 82% ± 1%; sensitivity = 65% ± 5%; specificity = 88% ± 2%; precision = 67% ± 3%; and F1 = 0.66 ± 0.02. The area under the receiver operating characteristic curve (AUROC) was approximately 0.89. The SOFA score was used on the same 1588 instances for sepsis diagnosis, and the result was inferior to our AI algorithm (AUROC = 0.596). CONCLUSION: Using real-time data, collected by EMR, from the ICU daily practice, our AI algorithm established with pre-selected features and XGBoost can provide a timely diagnosis of sepsis with an accuracy greater than 80%. AI algorithm also outperforms the SOFA score in sepsis diagnosis and exhibits practicality as clinicians can deploy appropriate treatment earlier. The early and precise response of this AI algorithm will result in cost reduction, outcome improvement, and benefit for healthcare systems, medical staff, and patients as well.

Statin Use and Influenza Vaccine Effectiveness in Persons >65 Years of Age, Taiwan.

Debates on whether statin use reduces the effectiveness of influenza vaccines against critical illness and death among persons >65 years of age continue. We conducted a study of 9,427,392 persons >65 years of age who did and did not receive influenza vaccinations during 12 consecutive influenza seasons, 2000-01 through 2011-12. Using data from Taiwan's National Health Insurance Research Database, we performed propensity score-matching to compare vaccinated persons with unvaccinated controls. After propensity score-matching, the vaccinated group had lower risks for in-hospital death from influenza and pneumonia and for hospitalization for pneumonia and influenza, circulatory conditions, and critical illnesses compared with the unvaccinated group. We stratified the 2 groups by statin use and analyzed data by interaction analysis and saw no statistically significant difference. We found that influenza vaccine effectively reduced risks for hospitalization and death in persons >65 years of age, regardless of statin use.

Association between renin-angiotensin-aldosterone system blockade and future osteoporotic fracture risk in hypertensive population: A population-based cohort study in Taiwan.

Tissue renin-angiotensin-aldosterone system (RAAS) activation in sites of osteoporosis had been demonstrated in animal studies; however, the possibility of RAAS blockade to prevent future osteoporotic fracture had rarely been verified in clinical studies. We Used the Taiwan Longitudinal Health insurance database 2000 to 2008, the cohort study comprised patients age over 40 with a recorded new diagnosis of hypertension between January 1, 2000 to December 31, 2008, in addition, patients who had diagnosis of osteoporosis before the date of cohort enter were excluded. After the definite diagnosis of hypertension, each patient was followed until osteoporotic fracture happened or the end of 2008. The occurrence of osteoporotic fracture was evaluated in patients who either were or without taking RAAS blockade agents. Cox proportional hazard regressions were used to evaluate the osteoporotic fracture incidence after adjusting for known confounding factors. In total, 57,132 hypertensive patients comprised the study cohort. Our study results showed that the incidence of osteoporosis fracture in the whole cohort was significantly higher in the RAAS blockade non-user group than the user group. This phenomenon was observed in both sex and all age categories. Sensitivity analysis further showed the concordant lower osteoporosis fracture risk in patients with various RAAS blockers usage durations; the risk of osteoporosis fracture was the lowest in those drug use >365 days when compared with the non-user cohort. In conclusion, our study result demonstrated the lower future osteoporotic fracture risk in hypertensive subjects who received long term RAAS blocker treatment.

Impact of Beta-Blocker Initiation Timing on Mortality Risk in Patients With Diabetes Mellitus Undergoing Noncardiac Surgery: A Nationwide Population-Based Cohort Study.

BACKGROUND: Relevant clinical studies have been small and have not convincingly demonstrated whether the perioperative initiation of beta-blockers should be considered in patients with diabetes mellitus undergoing noncardiac surgery. METHODS AND RESULTS: In this nationwide propensity score-matched study, we included patients with diabetes mellitus undergoing noncardiac surgery between 2000 and 2011 from Taiwan's National Health Insurance Research Database. Patients were classified as beta-blocker and non-beta-blocker cohorts. We further stratified beta-blocker users into cardioprotective beta-blocker (atenolol, bisoprolol, metoprolol, or carvedilol) and other beta-blocker users. To investigate time of initiation of beta-blocker use, initiation time was stratified into 2 periods (>30 and ≤30 days preoperatively). The outcomes of interest were in-hospital and 30-day mortality. After propensity score matching, we identified 50 952 beta-blocker users and 50 952 matched controls. Compared with non-beta-blocker users, cardioprotective beta-blocker users were associated with lower risks of in-hospital (odds ratio 0.75, 95% CI 0.68-0.82) and 30-day (odds ratio 0.75, 95% CI 0.70-0.81) mortality. Among initiation times, only the use of cardioprotective beta-blockers for >30 days was associated with decreased risk of in-hospital (odds ratio 0.72, 95% CI 0.65-0.78) and 30-day (odds ratio 0.72, 95% CI 0.66-0.78) mortality. Of note, use of other beta-blockers for ≤30 days before surgery was associated with increased risk of both in-hospital and 30-day mortality. CONCLUSIONS: The use of cardioprotective beta-blockers for >30 days before surgery was associated with reduced mortality risk, whereas short-term use of beta-blockers was not associated with differences in mortality in patients with diabetes mellitus.

Pyogenic liver abscess in end-stage renal disease patients: a nationwide longitudinal study.

End-stage renal disease (ESRD) patients are more prone to infectious disease because of their immunocompromised status. However, the association between pyogenic liver abscess (PLA) and ESRD remains not clear. The aim of our study is to evaluate the incidence, risk factors, and outcomes of PLA in ESRD patients. We recruited all incident ESRD patients from the Taiwan National Health Insurance database from 1998 to 2006. The incidence rate of PLA in ESRD patients was compared with that of a randomly selected non-ESRD control group matched for age, sex gender, Charlson comorbidity score, diabetes mellitus, and cirrhosis. Among the 57,761 incident dialysis patients, there were 538 cases of PLA. The incidence rate of PLA was 18.20 per 10,000 person-years in the ESRD cohort and 6.34 per 10,000 person-years in matched control cohort. The rate of PLA was significantly higher in the ESRD cohort (hazard ratio 3.63, 95% confidence interval 2.83-4.65, P < 0.001). The mortality rates of PLA were higher in the ESRD cohort than those in matched control cohort. Diabetes mellitus was an independent risk factor for mortality of PLA. Compared with non-ESRD patients, ESRD patients have a higher risk of PLA and poorer outcomes.

Effect of statin therapy on mortality in patients with infective endocarditis.

The aim of our study was to determine whether pre-emptive statin therapy was associated with improved outcome of infective endocarditis (IE). We conducted a nationwide, population-based, propensity score-matched cohort study with the Taiwan's National Health Insurance Research Database. All patients with IE between January 2000 and December 2010 were enrolled. The primary outcome was in-hospital mortality. The secondary outcome included all-cause mortality within the first 3 months, 6 months, and one year after the diagnosis of IE. Among 13,584 patients with IE, we applied propensity score-matching on a 1:4 ratio, in which 370 statin users were matched to 1,480 statin non-users. Compared with statin non-users, statin users had a significantly lower risk of in-hospital mortality (adjusted hazard ratio [aHR] 0.65, 95% confidence interval [CI], 0.49-0.86). The reduction in mortality from IE remained significant for follow-up 3 months (aHR 0.68, 95% CI, 0.53-0.88), 6 months (aHR 0.73, 95% CI, 0.58-0.91), and 12 months (aHR 0.68, 95% CI, 0.55-0.84). Statin therapy was associated with a reduced risk of ICU admission rates, shock events, the need for mechanical ventilation, but not significantly with the need for heart valvular replacement surgery. In conclusion, our study found that statin therapy is associated with a reduced risk of in-hospital and subsequent mortality of IE.

Folic acid supplementation in end-stage renal disease patients reduces total mortality rate.

BACKGROUND: Patients with end-stage renal disease (ESRD) are at high risk of cardiovascular disease and elevated serum homocysteine levels. Although folic acid supplementation has been documented to reduce serum homocysteine levels in ESRD patients, most trials of folic acid therapy for reducing cardiovascular diseases in ESRD patients have failed, mainly because of limited patient numbers. METHODS: We used the Taiwan National Health Insurance Research Database (NHIRD) to conduct a matched-pair retrospective cohort study to clarify whether folic acid supplementation benefits ESRD patient survival. Patients were divided into a folic acid supplementation group and a control group. All-cause and cardiovascular-related mortality rates between groups were compared. RESULTS: In total, 55,636 stable incident hemodialysis patients were identified from the database. Using a propensity score-matched method and intention-to-treat analysis, the survival rate of 17,000 patients with folic acid supplementation was compared with a 1:1 matched control group. The baseline demographic data and comorbid disease incidence between the 2 groups were comparable. During the study period, the mortality rate in the matched pair cohort was 35.5% (n = 6,030) over a mean follow-up period of 3.0 years, corresponding to a mortality rate of 12.8/100 patient-years. The all-cause mortality rates were 12.3 and 13.4/100 patient-years in the folic acid group and control group, respectively (p = 0.005). CONCLUSIONS: In adult hemodialysis patients, folic acid supplementation improves cardiovascular and all-cause mortality rates.