Acute kidney injury in patients with COVID-19 compared to those with influenza: a systematic review and meta-analysis

BackgroundCOVID-19 and influenza can both lead to acute kidney injury (AKI) as a common complication. However, no meta-analysis has been conducted to directly compare the incidence of AKI between hospitalized patients with COVID-19 and influenza. The objective of our study aims to investigate the incidence and outcomes of AKI among hospitalized patients between these two groups.Materials and methodsA systematic search of PubMed, Embase, and Cochrane databases was conducted from December 2019 to August 2023 to identify studies examining AKI and clinical outcomes among hospitalized patients with COVID-19 and influenza. The primary outcome of interest was the incidence of AKI, while secondary outcomes included in-hospital mortality, recovery from AKI, hospital and ICU stay duration. The quality of evidence was evaluated using Cochrane and GRADE methods.ResultsTwelve retrospective cohort studies, involving 17,618 hospitalized patients with COVID-19 and influenza, were analyzed. COVID-19 patients showed higher AKI incidence (29.37% vs. 20.98%, OR: 1.67, 95% CI 1.56–1.80, p < 0.01, I2 = 92.42%), and in-hospital mortality (30.95% vs. 5.51%, OR: 8.16, 95% CI 6.17–10.80, p < 0.01, I2 = 84.92%) compared to influenza patients with AKI. Recovery from AKI was lower in COVID-19 patients (57.02% vs., 80.23%, OR: 0.33, 95% CI 0.27–0.40, p < 0.01, I2 = 85.17%). COVID-19 patients also had a longer hospital stay (SMD: 0.69, 95% CI 0.65–0.72, p < 0.01, I2 = 98.94%) and longer ICU stay (SMD: 0.61, 95% CI 0.50–0.73, p < 0.01, I2 = 94.80%) than influenza patients. In our study, evidence quality was high (NOS score 7–9), with low certainty for AKI incidence and moderate certainty for recovery form AKI by GRADE assessment.ConclusionCOVID-19 patients had higher risk of developing AKI, experiencing in-hospital mortality, and enduring prolonged hospital/ICU stays in comparison to influenza patients. Additionally, the likelihood of AKI recovery was lower among COVID-19 patients

A proposed prognostic 7-day survival formula for patients with terminal cancer

<p>Abstract</p> <p>Background</p> <p>The ability to identify patients for hospice care results in better end-of-life care. To develop a validated prognostic scale for 7-day survival prediction, a prospective observational cohort study was made of patients with terminal cancer.</p> <p>Methods</p> <p>Patient data gathered within 24 hours of hospital admission included demographics, clinical signs and symptoms and their severity, laboratory test results, and subsequent survival data. Of 727 patients enrolled, data from 374 (training group) was used to develop a prognostic tool, with the other 353 serving as the validation group.</p> <p>Results</p> <p>Five predictors identified by multivariate logistic regression analysis included patient's cognitive status, edema, ECOG performance status, BUN and respiratory rate. A formula of the predictor model based on those five predictors was constructed. When probability was >0.2, death within 7 days was predicted in the training group and validation group, with sensitivity of 80.9% and 71.0%, specificity of 65.9% and 57.7%, positive predictive value of 42.6% and 26.8%, and negative predictive value (NPV) of 91.7% and 90.1%, respectively.</p> <p>Conclusion</p> <p>This predictor model showed a relatively high sensitivity and NPV for predicting 7-day survival among terminal cancer patients, and could increase patient satisfaction by improving end-of-life care.</p

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

METCAM Is a Potential Biomarker for Predicting the Malignant Propensity of and as a Therapeutic Target for Prostate Cancer

Prostate cancer is the second leading cause of cancer-related death worldwide. This is because it is still unknown why indolent prostate cancer becomes an aggressive one, though many risk factors for this type of cancer have been suggested. Currently, many diagnostic markers have been suggested for predicting malignant prostatic carcinoma cancer; however, only a few, such as PSA (prostate-specific antigen), Prostate Health Index (PHI), and PCA3, have been approved by the FDA. However, each biomarker has its merits as well as shortcomings. The serum PSA test is incapable of differentiating prostate cancer from BPH and also has an about 25% false-positive prediction rate for the malignant status of cancer. The PHI test has the potential to replace the PSA test for the discrimination of BPH from prostate cancer and for the prediction of high-grade cancer avoiding unnecessary biopsies; however, the free form of PSA is unstable and expensive. PCA3 is not associated with locally advanced disease and is limited in terms of its prediction of aggressive cancer. Currently, several urine biomarkers have shown high potential in terms of being used to replace circulating biomarkers, which require a more invasive method of sample collection, such as via serum. Currently, the combined multiple tumor biomarkers may turn out to be a major trend in the diagnosis and assessment of the treatment effectiveness of prostate cancer. Thus, there is still a need to search for more novel biomarkers to develop a perfect cocktail, which consists of multiple biomarkers, in order to predict malignant prostate cancer and follow the efficacy of the treatment. We have discovered that METCAM, a cell adhesion molecule in the Ig-like superfamily, has great potential regarding its use as a biomarker for differentiating prostate cancer from BPH, predicting the malignant propensity of prostate cancer at the early premalignant stage, and differentiating indolent prostate cancers from aggressive cancers. Since METCAM has also been shown to be able to initiate the spread of prostate cancer cell lines to multiple organs, we suggest that it may be used as a therapeutic target for the clinical treatment of patients with malignant prostate cancer

METCAM Is a Potential Biomarker for Predicting the Malignant Propensity of and as a Therapeutic Target for Prostate Cancer

Prostate cancer is the second leading cause of cancer-related death worldwide. This is because it is still unknown why indolent prostate cancer becomes an aggressive one, though many risk factors for this type of cancer have been suggested. Currently, many diagnostic markers have been suggested for predicting malignant prostatic carcinoma cancer; however, only a few, such as PSA (prostate-specific antigen), Prostate Health Index (PHI), and PCA3, have been approved by the FDA. However, each biomarker has its merits as well as shortcomings. The serum PSA test is incapable of differentiating prostate cancer from BPH and also has an about 25% false-positive prediction rate for the malignant status of cancer. The PHI test has the potential to replace the PSA test for the discrimination of BPH from prostate cancer and for the prediction of high-grade cancer avoiding unnecessary biopsies; however, the free form of PSA is unstable and expensive. PCA3 is not associated with locally advanced disease and is limited in terms of its prediction of aggressive cancer. Currently, several urine biomarkers have shown high potential in terms of being used to replace circulating biomarkers, which require a more invasive method of sample collection, such as via serum. Currently, the combined multiple tumor biomarkers may turn out to be a major trend in the diagnosis and assessment of the treatment effectiveness of prostate cancer. Thus, there is still a need to search for more novel biomarkers to develop a perfect cocktail, which consists of multiple biomarkers, in order to predict malignant prostate cancer and follow the efficacy of the treatment. We have discovered that METCAM, a cell adhesion molecule in the Ig-like superfamily, has great potential regarding its use as a biomarker for differentiating prostate cancer from BPH, predicting the malignant propensity of prostate cancer at the early premalignant stage, and differentiating indolent prostate cancers from aggressive cancers. Since METCAM has also been shown to be able to initiate the spread of prostate cancer cell lines to multiple organs, we suggest that it may be used as a therapeutic target for the clinical treatment of patients with malignant prostate cancer

Detection of Single Nucleotide Polymorphism (SNP) Variation of a Gene Sequence on Membrane-Based Lateral-Flow Strips

This study used appropriate primers to distinguish the gene model, HLA-A31:01, on membrane-based lateral-flow (MBLF) strips from its allele, which is with an SNP. Using primers designed with a mismatch base on one or two sides next to the SNP spot was verified as a good approach. In the optimal condition, the detection limits of 1~0.1 ng/μL nucleotides were in agreement with reports in the literature, and the intra- and inter-assay tests ensured the detection reproducibility of this approach with CV% of 2.5~15.9% and 1.7~14.7%, respectively. The detection specificity was also validated by the tests on the selected negative-control genes. The tests on MBLF strips in this study showed an easy, robust, reproducible, and reliable detection methodology for untrained personnel at care points with limited instrument and particularly for avoiding medications from faulty prescriptions

Electrophoresis-Enhanced Detection of Deoxyribonucleic Acids on a Membrane-Based Lateral Flow Strip Using Avian Influenza H5 Genetic Sequence as the Model

This study reports a simple strategy to detect a deoxyribonucleic acid (DNA) on a membrane-based lateral flow (MBLF) strip without tedious gel preparation, gel electrophoresis, and EtBr-staining processes. The method also enhances the detection signal of the genetic sample. A direct electric field was applied over two ends of the MBLF strips to induce an electrophoresis of DNAs through the strips. The signal enhancement was demonstrated by the detection of the H5 subtype of avian influenza virus (H5 AIV). This approach showed an excellent selectivity of H5 AIV from other two control species, Arabidopsis thaliana and human PSMA5. It also showed an effective signal repeatability and sensitivity over a series of analyte concentrations. Its detection limit could be enhanced, from 40 ng to 0.1 ng by applying 12 V. The nano-gold particles for the color development were labeled on the capture antibody, and UV-VIS and TEM were used to check if the labeling was successful. This detection strategy could be further developed to apply on the detection of drug-allergic genes at clinics or detection of infectious substances at incident sites by a simple manipulation with an aid of a mini-PCR machine and auxiliary kits

Apoptotic biliary epithelial cells and gut dysbiosis in the induction of murine primary biliary cholangitis

Primary biliary cholangitis (PBC) is a female-predominant liver autoimmune disease characterized by the specific immune-mediated destruction of the intrahepatic small bile duct. Although apoptosis of biliary epithelial cells (BECs) and alterations in gut microbiota are observed in patients with PBC, it is still unclear whether these events happen in the early stage and cause the breakdown of tolerance in PBC. In this study, we examined the early events in the loss of tolerance in our well-defined 2-OA-OVA-induced murine autoimmune cholangitis (AIC) model. We report herein that apoptosis of BECs was notable in the early stage of murine AIC. An altered gut microbiota, in particular, an increased percentage of gram-positive Firmicutes in AIC mice was also observed. BECs in AIC mice expressed adhesion molecule ICAM-1, cytokines/chemokines TNF-α, CCL2, CXCL9, CXCL10, and toll-like receptor (TLR) 2. Moreover, BECs treated with TLR2 ligand had elevated apoptosis and CXCL10 production. These data collectively suggest a new mechanism of tolerance breakdown in AIC. Altered gut microbiota induces apoptosis of BECs through TLR2 signaling. BECs secrete chemokines to recruit CD8 T cells to damage BECs further