Cardiovascular subphenotypes in patients with COVID-19 pneumonitis whose lungs are mechanically ventilated: a single-centre retrospective observational study

Unsupervised clustering methods of transthoracic echocardiography variables have not been used to characterise circulatory failure mechanisms in patients with COVID‐19 pneumonitis. We conducted a retrospective, single‐centre cohort study in ICU patients with COVID‐19 pneumonitis whose lungs were mechanically ventilated and who underwent transthoracic echocardiography between March 2020 and May 2021. We performed latent class analysis of echocardiographic and haemodynamic variables. We characterised the identified subphenotypes by comparing their clinical parameters, treatment responses and 90‐day mortality rates. We included 305 patients with a median (IQR [range]) age 59 (49–66 [16–83]) y. Of these, 219 (72%) were male, 199 (65%) had moderate acute respiratory distress syndrome and 113 (37%) did not survive more than 90 days. Latent class analysis identified three cardiovascular subphenotypes: class 1 (52%; normal right ventricular function); class 2 (31%; right ventricular dilation with mostly preserved systolic function); and class 3 (17%; right ventricular dilation with systolic impairment). The three subphenotypes differed in their clinical characteristics and response to prone ventilation and outcomes, with 90‐day mortality rates of 22%, 42% and 73%, respectively (p < 0.001). We conclude that the identified subphenotypes aligned with right ventricular pathophysiology rather than the accepted definitions of right ventricular dysfunction, and these identified classifications were associated with clinical outcomes.

Association of clinical factors and recent anticancer therapy with COVID-19 severity among patients with cancer: a report from the COVID-19 and Cancer Consortium

BACKGROUND

Patients with cancer may be at high risk of adverse outcomes from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We analyzed a cohort of patients with cancer and coronavirus 2019 (COVID-19) reported to the COVID-19 and Cancer Consortium (CCC19) to identify prognostic clinical factors, including laboratory measurements and anticancer therapies.

PATIENTS AND METHODS

Patients with active or historical cancer and a laboratory-confirmed SARS-CoV-2 diagnosis recorded between 17 March and 18 November 2020 were included. The primary outcome was COVID-19 severity measured on an ordinal scale (uncomplicated, hospitalized, admitted to intensive care unit, mechanically ventilated, died within 30 days). Multivariable regression models included demographics, cancer status, anticancer therapy and timing, COVID-19-directed therapies, and laboratory measurements (among hospitalized patients).

RESULTS

A total of 4966 patients were included (median age 66 years, 51% female, 50% non-Hispanic white); 2872 (58%) were hospitalized and 695 (14%) died; 61% had cancer that was present, diagnosed, or treated within the year prior to COVID-19 diagnosis. Older age, male sex, obesity, cardiovascular and pulmonary comorbidities, renal disease, diabetes mellitus, non-Hispanic black race, Hispanic ethnicity, worse Eastern Cooperative Oncology Group performance status, recent cytotoxic chemotherapy, and hematologic malignancy were associated with higher COVID-19 severity. Among hospitalized patients, low or high absolute lymphocyte count; high absolute neutrophil count; low platelet count; abnormal creatinine; troponin; lactate dehydrogenase; and C-reactive protein were associated with higher COVID-19 severity. Patients diagnosed early in the COVID-19 pandemic (January-April 2020) had worse outcomes than those diagnosed later. Specific anticancer therapies (e.g. R-CHOP, platinum combined with etoposide, and DNA methyltransferase inhibitors) were associated with high 30-day all-cause mortality.

CONCLUSIONS

Clinical factors (e.g. older age, hematological malignancy, recent chemotherapy) and laboratory measurements were associated with poor outcomes among patients with cancer and COVID-19. Although further studies are needed, caution may be required in utilizing particular anticancer therapies.

CLINICAL TRIAL IDENTIFIER

NCT04354701.

A time-sensitive analysis of the prognostic utility of vasopressor dose in septic shock

It is unclear whether the association between vasopressor dose and mortality is affected by duration of administration. We examined whether prognostication in septic shock is feasible through the use of daily median vasopressor doses. We undertook a single-centre retrospective cohort study. We included patients with a diagnosis of septic shock admitted to the intensive care unit at Queen Elizabeth Hospital, Birmingham, UK, between April 2016 and July 2019. The primary outcome measure was 90-day mortality. We defined vasopressor dose as the median norepinephrine equivalent dose (equivalent infusion rates of all vasopressors and inotropes) recorded for each day, for the first four days of septic shock. We divided patients into groups by vasopressor dose quintiles and calculated their 90-day mortality rate. We examined area under the receiver operator characteristic curves for prognostic ability. In total, 844 patients were admitted with septic shock and had a 90-day mortality of 43% (n = 358). Over the first four days, median vasopressor dose decreased in 93% of survivors and increased in 56% of non-survivors. The mortality rate associated with a given vasopressor dose quintile increased on sequential days of septic shock. The area under the receiver operator characteristic curves of daily median vasopressor dose against mortality increased from day 1 to day 4 (0.67 vs. 0.86, p < 0.0001). By day 4, a median daily vasopressor dose > 0.05 μg.kg^-1 .min^-1 had an 80% sensitivity and specificity for mortality. The prognostic utility of vasopressor dose improved considerably with shock duration. Prolonged administration of small vasopressor doses was associated with a high attributable mortality.

Renal impairment and its impact on clinical outcomes in patients who are critically ill with COVID-19: a multicentre observational study

Renal impairment is common in patients who are critically ill with coronavirus disease-19 (COVID-19). We examined the association between acute and chronic kidney disease with clinical outcomes in 372 patients with coronavirus disease-19 admitted to four regional intensive care units between 10 March 2020 and 31 July 2020. A total of 216 (58%) patients presented with COVID-19 and renal impairment. Acute kidney injury and/or chronic kidney disease was associated with greater in-hospital mortality compared with patients with preserved renal function (107/216 patients (50%) (95%CI 44-57) vs. 32/156 (21%) (95%CI 15-28), respectively; p < 0.001, relative risk 2.4 (95%CI 1.7-3.4)). Mortality was greatest in patients with renal transplants (6/7 patients (86%) (95%CI 47-100)). Mortality rates increased in patients with worsening renal injury according to the Kidney Disease: Improving Global Outcomes classification: stage 0 mortality 33/157 patients (21%) (95%CI 15-28) vs. stages 1-3 mortality 91/186 patients (49%) (95%CI 42-56); p < 0.001, relative risk 2.3 (95%CI 1.7-3.3). Survivors were less likely to require renal replacement therapy compared with non-survivors (57/233 patients (24%) vs. 64/139 patients (46%), respectively; p < 0.001, relative risk 1.9 (95%CI 1.4-2.5)). One-fifth of survivors who required renal replacement therapy acutely in intensive care continued to require renal support following discharge. Our data demonstrate that renal impairment in patients admitted to intensive care with COVID-19 is common and is associated with a high mortality and requirement for on-going renal support after discharge from critical care. Our findings have important implications for future pandemic planning in this patient cohort.

Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods

Quantification of fibrillar collagen organization has given new insight into the possible role of collagen topology in many diseases and has also identified candidate image-based bio-markers in breast cancer and pancreatic cancer. We have been developing collagen quantification tools based on the curvelet transform (CT) algorithm and have demonstrated this to be a powerful multiscale image representation method due to its unique features in collagen image denoising and fiber edge enhancement. In this paper, we present our CT-based collagen quantification software platform with a focus on new features and also giving a detailed description of curvelet-based fiber representation. These new features include C++-based code optimization for fast individual fiber tracking, Java-based synthetic fiber generator module for method validation, automatic tumor boundary generation for fiber relative quantification, parallel computing for large-scale batch mode processing, region-of-interest analysis for user-specified quantification, and pre- and post-processing modules for individual fiber visualization. We present a validation of the tracking of individual fibers and fiber orientations by using synthesized fibers generated by the synthetic fiber generator. In addition, we provide a comparison of the fiber orientation calculation on pancreatic tissue images between our tool and three other quantitative approaches. Lastly, we demonstrate the use of our software tool for the automatic tumor boundary creation and the relative alignment quantification of collagen fibers in human breast cancer pathology images, as well as the alignment quantification of in vivo mouse xenograft breast cancer images.

Chronic Pruritus: A Review of Neurophysiology and Associated Immune Neuromodulatory Treatments

Chronic pruritus remains a difficult condition to treat with many non-specific therapeutic options. Recent scientific discoveries have elucidated the physiology associated with pruritus. Combined with clinical and experimental trials with immune-modulatory agents, chronic pruritus now has novel treatment options with known mechanisms of action. This review goes over recent scientific progress in understanding the molecular mechanisms governing pruritus, the cross-talk between the immune and nervous systems that regulate itch, and central nervous pathways and projections affected by itch. In light of these recent discoveries, we briefly discuss a growing body of data from relevant clinical trials investigating immunomodulatory drugs targeting specific interleukin receptors (IL-4/13/31) and intracellular signaling (e.g., Janus kinase) pathways. We focus on the physiological processes that control this complex physical and emotional experience, as well as the role of newer drugs used to treat chronic itch.

A Natural Language Interface for Dissemination of Reproducible Biomedical Data Science

Computational tools in the form of software packages are burgeoning in the field of medical imaging and biomedical research. These tools enable biomedical researchers to analyze a variety of data using modern machine learning and statistical analysis techniques. While these publicly available software packages are a great step towards a multiplicative increase in the biomedical research productivity, there are still many open issues related to validation and reproducibility of the results. A key gap is that while scientists can validate domain insights that are implicit in the analysis, the analysis itself is coded in a programming language and that domain scientist may not be a programmer. Thus, there is no/limited direct validation of the program that carries out the desired analysis. We propose a novel solution, building upon recent successes in natural language understanding, to address this problem. Our platform allows researchers to perform, share, reproduce and interpret the analysis pipelines and results via natural language. While this approach still requires users to have a conceptual understanding of the techniques, it removes the burden of programming syntax and thus lowers the barriers to advanced and reproducible neuroimaging and biomedical research.

Caprine leptospirosis: Hematobiochemical and urinalyses studies

AIM

The present study was designed to evaluate clinicopathological alterations in naturally occurring leptospirosis in goats of South Gujarat region, Gujarat.

MATERIALS AND METHODS

A total 459 blood/serum and 292 urine samples were collected from different districts of South Gujarat region, India. Blood/serum and urine samples were subjected to hematobiochemical analyses and urinalyses. The serum samples were screened for anti-leptospiral antibodies using the microscopic agglutination test (MAT). On the bases of presence or absence of anti-leptospiral antibodies in serum, seropositive and seronegative groups were made. The results were analyzed using standard statistical methods to know pathological changes in the disease.

RESULTS

In MAT, out of 459, 116 goats were seropositive, and 343 were seronegative. In hematobiochemical analyses, statistically significant (p<0.01) decrease in values of packed cell volume, hemoglobin (Hb) concentration, mean corpuscular Hb concentration and total protein and increased activity/level of alanine aminotransferase, aspartate aminotransferase and total bilirubin between seropositive and seronegative goats were noted. Urinalyses did not reveal any specific changes. In the dark field microscopy, urine samples were found to be negative for leptospires.

CONCLUSION

Hematobiochemical changes noted in seropositive goats were indicative of hepatic damage, and this knowledge would aid in the therapeutic management of the disease.

Identification of cross-species shared transcriptional networks of diabetic nephropathy in human and mouse glomeruli

Murine models are valuable instruments in defining the pathogenesis of diabetic nephropathy (DN), but they only partially recapitulate disease manifestations of human DN, limiting their utility. To define the molecular similarities and differences between human and murine DN, we performed a cross-species comparison of glomerular transcriptional networks. Glomerular gene expression was profiled in patients with early type 2 DN and in three mouse models (streptozotocin DBA/2, C57BLKS db/db, and eNOS-deficient C57BLKS db/db mice). Species-specific transcriptional networks were generated and compared with a novel network-matching algorithm. Three shared human-mouse cross-species glomerular transcriptional networks containing 143 (Human-DBA STZ), 97 (Human-BKS db/db), and 162 (Human-BKS eNOS(-/-) db/db) gene nodes were generated. Shared nodes across all networks reflected established pathogenic mechanisms of diabetes complications, such as elements of Janus kinase (JAK)/signal transducer and activator of transcription (STAT) and vascular endothelial growth factor receptor (VEGFR) signaling pathways. In addition, novel pathways not previously associated with DN and cross-species gene nodes and pathways unique to each of the human-mouse networks were discovered. The human-mouse shared glomerular transcriptional networks will assist DN researchers in selecting mouse models most relevant to the human disease process of interest. Moreover, they will allow identification of new pathways shared between mice and humans.

Toxicopathological evaluation in Wistar rats (Rattus norvegicus) following repeated oral exposure to acephate

The present study was carried out to evaluate the effects of exposure at different doses of acephate on hematology, blood biochemistry, oxidative stress and immune system of Wistar rats. The experiment was carried out on 40 Wistar rats, which were divided in four groups. Animals of the three treatment groups were given with different sublethal doses (1/40th, 1/20th, 1/10th of lethal dose 50 value) of acephate by oral gavage. The hematology, blood biochemistry, oxidative stress marker, humoral immune response and cell-mediated immunity were evaluated following acephate exposure. Significant alteration in hematological parameters was not observed following different doses of acephate; however, significant alteration in alkaline phosphatase, gamma glutamyl transferase, acetyl cholinesterase, lipid peroxidase and superoxide dismutase was observed in medium- and high-dose group animals. Nonsignificant decrease in antibody titer in animals exposed to high dose has been observed compared with animals of control group. However, significant alteration in cell-mediated immunity was not observed in animals treated with acephate at different doses.

Exploratory data mining analysis identifying subgroups of patients with depression who are at high risk for suicide

OBJECTIVE

Although prior research has identified a number of separate risk factors for suicide among patients with depression, little is known about how these factors may interact to modify suicide risk. Using an empirically based decision tree analysis for a large national sample of Veterans Affairs (VA) health system patients treated for depression, we identified subgroups with particularly high or low rates of suicide.

METHOD

We identified 887,859 VA patients treated for depression between April 1, 1999, and September 30, 2004. Randomly splitting the data into 2 samples (primary and replication samples), we developed a decision tree for the primary sample using recursive partitioning. We then tested whether the groups developed within the primary sample were associated with increased suicide risk in the replication sample.

RESULTS

The exploratory data analysis produced a decision tree with subgroups of patients at differing levels of risk for suicide. These were identified by a combination of factors including a co-occurring substance use disorder diagnosis, male sex, African American race, and psychiatric hospitalization in the past year. The groups developed as part of the decision tree accurately discriminated between those with and without suicide in the replication sample. The patients at highest risk for suicide were those with a substance use disorder who were non-African American and had an inpatient psychiatric stay within the past 12 months.

CONCLUSIONS

Study findings suggest that the identification of depressed patients at increased risk for suicide is improved through the examination of higher order interactions between potential risk factors.

BatchGenAna: a batch platform for large-scale genomic analysis of mammalian small RNAs

An increasing number of small RNAs have been discovered in mammals. However, their primary transcripts and upstream regulatory networks remain largely to be determined. Genomic analysis of small RNAs facilitates identification of their primary transcripts, and hence contributes to researches of their upstream regulatory networks. We here report a batch platform, BatchGenAna, which is specifically designed for large-scale genomic analysis of mammalian small RNAs. It can map and annotate for as many as 1000 small RNAs or 10,000 genomic loci of small RNAs at a time. It provides genomic features including RefSeq genes, mRNAs, ESTs and repeat elements in tabular and graphical results. It also allows extracting flanking sequences of submitted queries, specified genomic regions and host transcripts, which facilitates subsequent analysis such as scanning transcription factor binding sites in upstream sequences and poly(A) signals in downstream sequences. Besides small RNA fields, BatchGenAna can also be applied to other research fields, e.g. in silico analysis of target genes of transcription factors.

ProbeMatch: rapid alignment of oligonucleotides to genome allowing both gaps and mismatches

SUMMARY

We have developed a tool, called ProbeMatch, for matching a large set of oligonucleotide sequences against a genome database using gapped alignments. Unlike most of the existing tools such as ELAND which only perform ungapped alignments allowing at most two mismatches, ProbeMatch generates both ungapped and gapped alignments allowing up to three errors including insertion, deletion and mismatch. To speedup sequence alignment, ProbeMatch uses gapped q-grams and q-grams of various patterns to identify target hits to a query sequence. This approach results in fewer initial sequences to examine with no loss in sensitivity. ProbeMatch has been used to align 169,095 Illumina GAII reads against the human genome, which could not be mapped by ELAND, and found alignments for 28,625 reads of the 169,095 reads in less than 3 h.

AVAILABILITY

Source code is freely available at (http://www.cs.wisc.edu/~jignesh/probematch/).

Efficient evaluation of radial queries using the target tree

We propose a novel indexing structure, called the target tree, which is designed to answer a new type of spatial query, called the radial query. A radial query finds all objects in the spatial data set that intersect with line segments emanating from a single target point. Many biomedical applications use radial queries, including neurosurgical planning. A target tree uses a regular hierarchical decomposition of space using wedge shapes that emanate from the target point. We compare the target tree with the R*-tree and quadtree, and show that the target tree is significantly faster than these methods.

Thiazide-induced subtle renal injury not observed in states of equivalent hypokalemia

Hydrochlorothiazide (HCTZ) is used to manage hypertension and heart failure; however, its side effects include mild hypokalemia, metabolic abnormalities, and volume depletion, which might have deleterious effects on renal and endothelial function. We studied whether HCTZ cause renal injury and/or altered vasoreactivity and if these changes are hypokalemia-dependent. Rats were given a normal diet or a diet moderately low in potassium K+ with or without HCTZ. Animals fed either a low K+ diet alone or HCTZ developed mild hypokalemia. There was no significant difference in systolic blood pressure in the different treatment groups. All three groups with hypokalemia had mild proteinuria; low K(+)-HCTZ rats had reduced creatinine clearance. HCTZ-treated rats displayed hypomagnesemia, hypertriglyceridemia, hyperglycemia, insulin resistance, and hyperaldosteronism. No renal injury was observed in the groups without HCTZ; however, increased kidney weight, glomerular ischemia, medullary injury, and cortical oxidative stress were seen with HCTZ treatment. Endothelium-dependent vasorelaxation was reduced in all hypokalemic groups and correlated with reduced serum K+, serum, and urine nitric oxide. Our results show that HCTZ is associated with greater renal injury for the same degree of hypokalemia as the low K+ diet, suggesting that factors such as chronic ischemia and hyperaldosteronism due to volume depletion may be responsible agents. We also found impaired endothelium-dependent vasorelaxation was linked to mild hypokalemia.

SAGA: a subgraph matching tool for biological graphs

MOTIVATION

With the rapid increase in the availability of biological graph datasets, there is a growing need for effective and efficient graph querying methods. Due to the noisy and incomplete characteristics of these datasets, exact graph matching methods have limited use and approximate graph matching methods are required. Unfortunately, existing graph matching methods are too restrictive as they only allow exact or near exact graph matching. This paper presents a novel approximate graph matching technique called SAGA. This technique employs a flexible model for computing graph similarity, which allows for node gaps, node mismatches and graph structural differences. SAGA employs an indexing technique that allows it to efficiently evaluate queries even against large graph datasets.

RESULTS

SAGA has been used to query biological pathways and literature datasets, which has revealed interesting similarities between distinct pathways that cannot be found by existing methods. These matches associate seemingly unrelated biological processes, connect studies in different sub-areas of biomedical research and thus pose hypotheses for new discoveries. SAGA is also orders of magnitude faster than existing methods.

AVAILABILITY

SAGA can be accessed freely via the web at http://www.eecs.umich.edu/saga. Binaries are also freely available at this website.

A framework for protein structure classification and identification of novel protein structures

Background

Protein structure classification plays a central role in understanding the function of a protein molecule with respect to all known proteins in a structure database. With the rapid increase in the number of new protein structures, the need for automated and accurate methods for protein classification is increasingly important.

Results

In this paper we present a unified framework for protein structure classification and identification of novel protein structures. The framework consists of a set of components for comparing, classifying, and clustering protein structures. These components allow us to accurately classify proteins into known folds, to detect new protein folds, and to provide a way of clustering the new folds. In our evaluation with SCOP 1.69, our method correctly classifies 86.0%, 87.7%, and 90.5% of new domains at family, superfamily, and fold levels. Furthermore, for protein domains that belong to new domain families, our method is able to produce clusters that closely correspond to the new families in SCOP 1.69. As a result, our method can also be used to suggest new classification groups that contain novel folds.

Conclusion

We have developed a method called proCC for automatically classifying and clustering domains. The method is effective in classifying new domains and suggesting new domain families, and it is also very efficient. A web site offering access to proCC is freely available at