Should We Fiddle with Gut Microbiome in Critically Ill?

The gut that we took for granted in the critically ill, as just a conduit for food passage has over the decade or so shown us that it is an active endocrine and exocrine organ with over 40 trillion microorganisms living commensally within it. This cosmos of microorganisms that is called the gut microbiome comprises roughly 1,000 different species and put together is more DNA than the entire human genome. Under normal circumstances, in a healthy individual multiple elements of the gut viz intestinal epithelium, gut barrier function, the microbiomes, all put together offer protection against infection and this is crucial in maintenance of health. Any change to the norm, be it in the form of surgical interventions, the introduction of medications, or the pathophysiological effects of systemic disease leads to a 360° alteration in this finely construed ecosystem leading to devastating effects that go beyond the boundaries of the gut itself. Intestinal epithelium helps to absorb nutrients as well as acts as the coordinator of mucosal immunity (first line of immune defense). During ill health, gut epithelial apoptosis occurs, alterations happen in the tight epithelial junctions leading to loss of gut barrier function and loss of the mucosal immunity leading to mucosal damage and hyperpermeability. Lastly, the microbiome is transformed into a pathobiome, with resultant increase in pathogenic bacteria and induction of virulence in commensal gut bacteria. Multiple organ damage starts to set in, caused by toxins leaving the intestine via both portal blood flow and mesenteric lymph. This review article traces the gut microbiomic ecology in health and sickness, modern tools that are used to manipulate gut microbiome in the search for the prevention and treatment of critical illness and will explore if appropriate manipulation of gut microbiome can influence or modulate the course of critical illness. How to cite this article: Venkatachalam B, Abraham BK. Should We Fiddle with Gut Microbiome in Critically Ill? Indian J Crit Care Med 2020;24(Suppl 4):S211-S214.

Colistin Nephrotoxicity in Adults: Single Centre Large Series from India.

CONTEXT: Limited Indian data are available on the rate of colistin nephrotoxicity and other risk factors contributing to the development of this important side effect. AIM: This study aims to generate data on colistin nephrotoxicity from a large cohort of Indian patients. DESIGN: Retrospective cohort study. MATERIALS AND METHODS: Case record analysis of patients who received colistin, in an oncology center in India, between January 2011 and December 2015. Nephrotoxicity was assessed using risk, injury, failure, loss, and end-stage (RIFLE) criteria. STATISTICAL ANALYSIS: P < 0.05 was considered as statistically significant. RESULTS: Out of the 229 patients, 13.1% (30/229) developed abnormal RIFLE. Abnormal RIFLE group (n = 30), in comparison to the normal renal function group (n = 199), had higher number of patients in intensive care unit (ICU) (96% vs. 79%, P = 0.02), higher Acute Physiology and Chronic Health Evaluation (APACHE II) score (23 vs. 19 P = 0.0001), Charlson score (5.9 vs. 4.3, P = 0.001), mechanical ventilation (90% vs. 67%, P = 0.016), 28 days mortality (63% vs. 25%, P = 0.0001), and abnormal baseline creatinine (36% vs. 8%, P = 0.001). Coadministration of vancomycin had higher rates of nephrotoxicity (P = 0.039). There was no significant difference in nephrotoxicity between 6 and 9 MU/day dosing pattern (8.8% vs. 13.8%, P = 0.058). CONCLUSION: Nephrotoxicity rate in our retrospective single center large series of patients receiving colistin was 13.1%. Patients with abnormal baseline creatinine, ICU stay, and higher disease severity are at higher risk of nephrotoxicity while on colistin. A daily dose of 9 million does not significantly increase nephrotoxicity compared to the 6 million. Concomitant administration of vancomycin with colistin increases the risk of nephrotoxicity.

Monotherapy versus Combination Therapy against Nonbacteremic Carbapenem-resistant Gram-negative Infections: A Retrospective Observational Study.

BACKGROUND: Superiority of colistin-carbapenem combination therapy (CCCT) over colistin monotherapy (CMT) against carbapenem-resistant Gram-negative bacterial (CRGNB) infections is not conclusively proven. AIM: The aim of the current study was to analyze the effectiveness of both strategies against CRGNB nonbacteremic infections. DESIGN: This was a retrospective observational cohort study. SUBJECTS AND METHODS: Case record analysis of patients who had CRGNB nonbacteremic infections identified over a period of 4 years (January 2012-December 2015) was done by medical record review at a tertiary care center in India. STATISTICAL ANALYSIS: P < 0.05 was considered as significant. Multivariate analysis was performed using Cox regression. RESULTS: Out of 153 patients (pneumonia 115, urinary tract infection 17, complicated skin and soft-tissue infection 18, intra-abdominal infection 1, and meningitis 2), 92 patients received CCCT and 61 received CMT. Univariate analysis revealed higher Acute Physiology and Chronic Health Evaluation II (APACHE II) score, pneumonia as the diagnosis, and Klebsiella as the causative organism to be the risk factors for higher 28-day mortality (P = 0.036, 0.006, 0.016, respectively). Combination therapy had no significant impact on mortality (odds ratio [OR] = 0.91, 95% confidence interval [CI] = 0.327-2.535, P = 0.857). Multivariate analysis revealed that higher APACHE II score and infection due to Klebsiella were found to be independent risk factors for higher mortality (OR = 3.16 and 4.9, 95% CI = 1.34-7.4 and 2.19-11.2, P = 0.008 and 0.0001, respectively). CONCLUSIONS: In our retrospective single-center series of CRGNB nonbacteremic infections, CCCT was not superior to CMT. Multicenter large observational studies or prospective randomized clinical trials are the need of the hour.

Faster algorithms for RNA-folding using the Four-Russians method.

BACKGROUND: The secondary structure that maximizes the number of non-crossing matchings between complimentary bases of an RNA sequence of length n can be computed in O(n3) time using Nussinov's dynamic programming algorithm. The Four-Russians method is a technique that reduces the running time for certain dynamic programming algorithms by a multiplicative factor after a preprocessing step where solutions to all smaller subproblems of a fixed size are exhaustively enumerated and solved. Frid and Gusfield designed an O(n3logn) algorithm for RNA folding using the Four-Russians technique. In their algorithm the preprocessing is interleaved with the algorithm computation. THEORETICAL RESULTS: We simplify the algorithm and the analysis by doing the preprocessing once prior to the algorithm computation. We call this the two-vector method. We also show variants where instead of exhaustive preprocessing, we only solve the subproblems encountered in the main algorithm once and memoize the results. We give a simple proof of correctness and explore the practical advantages over the earlier method.The Nussinov algorithm admits an O(n2) time parallel algorithm. We show a parallel algorithm using the two-vector idea that improves the time bound to O(n2logn). PRACTICAL RESULTS: We have implemented the parallel algorithm on graphics processing units using the CUDA platform. We discuss the organization of the data structures to exploit coalesced memory access for fast running times. The ideas to organize the data structures also help in improving the running time of the serial algorithms. For sequences of length up to 6000 bases the parallel algorithm takes only about 2.5 seconds and the two-vector serial method takes about 57 seconds on a desktop and 15 seconds on a server. Among the serial algorithms, the two-vector and memoized versions are faster than the Frid-Gusfield algorithm by a factor of 3, and are faster than Nussinov by up to a factor of 20. The source-code for the algorithms is available at http://github.com/ijalabv/FourRussiansRNAFolding.

Untangling tanglegrams: comparing trees by their drawings.

A tanglegram is a pair of trees on the same set of leaves with matching leaves in the two trees joined by an edge. Tanglegrams are widely used in biology--to compare evolutionary histories of host and parasite species and to analyze genes of species in the same geographical area. We consider optimization problems in tanglegram drawings. We show a linear time algorithm to decide if a tanglegram admits a planar embedding by a reduction to the planar graph drawing problem. This problem was also studied by Fernau et al. A similar reduction to a graph crossing problem also helps to solve an open problem they posed, showing a fixed-parameter tractable algorithm for minimizing the number of crossings over all d-ary trees. For the case where one tree is fixed, we show an O(n log n) algorithm to determine the drawing of the second tree that minimizes the number of crossings. This improves the bound from earlier methods. We introduce a new optimization criterion using Spearman's footrule distance and give an O(n²) algorithm. We also show integer programming formulations to quickly obtain tanglegram drawings that minimize the two optimization measures discussed. We prove lower bounds on the maximum gap between the optimal solution and the heuristic of Dwyer and Schreiber to minimize crossings.