Acute kidney injury (AKI) is a complex disorder for which currently there is no accepted definition. Having a uniform standard for diagnosing and classifying AKI would enhance our ability to manage these patients. Future clinical and translational research in AKI will require collaborative networks of investigators drawn from various disciplines, dissemination of information via multidisciplinary joint conferences and publications, and improved translation of knowledge from pre-clinical research. We describe an initiative to develop uniform standards for defining and classifying AKI and to establish a forum for multidisciplinary interaction to improve care for patients with or at risk for AKI. Members representing key societies in critical care and nephrology along with additional experts in adult and pediatric AKI participated in a two day conference in Amsterdam, The Netherlands, in September 2005 and were assigned to one of three workgroups. Each group's discussions formed the basis for draft recommendations that were later refined and improved during discussion with the larger group. Dissenting opinions were also noted. The final draft recommendations were circulated to all participants and subsequently agreed upon as the consensus recommendations for this report. Participating societies endorsed the recommendations and agreed to help disseminate the results. The term AKI is proposed to represent the entire spectrum of acute renal failure. Diagnostic criteria for AKI are proposed based on acute alterations in serum creatinine or urine output. A staging system for AKI which reflects quantitative changes in serum creatinine and urine output has been developed. We describe the formation of a multidisciplinary collaborative network focused on AKI. We have proposed uniform standards for diagnosing and classifying AKI which will need to be validated in future studies. The Acute Kidney Injury Network offers a mechanism for proceeding with efforts to improve patient outcomes.

Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury

Double-strand DNA breaks are common events in eukaryotic cells, and there are two major pathways for repairing them: homologous recombination (HR) and nonhomologous DNA end joining (NHEJ). The various causes of double-strand breaks (DSBs) result in a diverse chemistry of DNA ends that must be repaired. Across NHEJ evolution, the enzymes of the NHEJ pathway exhibit a remarkable degree of structural tolerance in the range of DNA end substrate configurations upon which they can act. In vertebrate cells, the nuclease, DNA polymerases, and ligase of NHEJ are the most mechanistically flexible and multifunctional enzymes in each of their classes. Unlike repair pathways for more defined lesions, NHEJ repair enzymes act iteratively, act in any order, and can function independently of one another at each of the two DNA ends being joined. NHEJ is critical not only for the repair of pathologic DSBs as in chromosomal translocations, but also for the repair of physiologic DSBs created during variable (diversity) joining [V(D)J] recombination and class switch recombination (CSR). Therefore, patients lacking normal NHEJ are not only sensitive to ionizing radiation (IR), but also severely immunodeficient.

/pdf/the-mechanism-of-double-strand-dna-break-repair-by-the-2f4y3sj9kf.pdf

The Mechanism of Double-Strand DNA Break Repair by the Nonhomologous DNA End-Joining Pathway

The AUDIT Alcohol Consumption Questions (AUDIT-C) An Effective Brief Screening Test for Problem Drinking

Spatial Organization of the Mouse Genome and Its Role in Recurrent Chromosomal Translocations

In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.

https://www.thelancet.com/pdfs/journals/lanonc/PIIS1470-2045(19)30154-8.pdf

Digital pathology and artificial intelligence

Objectives: While clinical laboratories report most test results as individual numbers, findings, or observations, clinical diagnosis usually relies on the results of multiple tests. Clinical decision support that integrates multiple elements of laboratory data could be highly useful in enhancing laboratory diagnosis.

Methods: Using the analyte ferritin in a proof of concept, we extracted clinical laboratory data from patient testing and applied a variety of machine-learning algorithms to predict ferritin test results using the results from other tests. We compared predicted with measured results and reviewed selected cases to assess the clinical value of predicted ferritin.

Results: We show that patient demographics and results of other laboratory tests can discriminate normal from abnormal ferritin results with a high degree of accuracy (area under the curve as high as 0.97, held-out test data). Case review indicated that predicted ferritin results may sometimes better reflect underlying iron status than measured ferritin.

Conclusions: These findings highlight the substantial informational redundancy present in patient test results and offer a potential foundation for a novel type of clinical decision support aimed at integrating, interpreting, and enhancing the diagnostic value of multianalyte sets of clinical laboratory test results.

/pdf/using-machine-learning-to-predict-laboratory-test-results-2hi66nb1px.pdf

Jason M. Baron

Papers

Using Machine Learning to Predict Laboratory Test Results.

3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data.

Computational pathology: an emerging definition.

Computerized provider order entry in the clinical laboratory

The role of informatics and decision support in utilization management