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opencc-by-4.0Dec 2022View details →

Figure 2 in In vitro study of antimicrobial activity of some plant seeds against bacterial strains causing food poisoning diseases

Figure 2. MIC's of the effective plant seeds powder against S. aureus and K. pneumonia, ± standard error.

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Commensal microbiome dysbiosis in keloid disease

Wound healing is an intensely studied topic involved in many relevant pathophysiological processes, including fibrosis. Despite the large interest in fibrosis, the network that related to commensal microbiota and skin fibrosis remain mysterious. Here, we pay attention to keloid, a classical yet intractable skin fibrotic disease to establish the association between commensal microbiota to scaring tissue. Our histological data reveal the presence of microbiota in the keloids. 16S rRNA sequencing characterize microbial composition and divergence between the pathological and normal skin tissue. Our research provides insights into the pathology of human fibrotic diseases, advocating commensal bacteria and IL-8 signaling as useful targets in future interventions of recurrent keloid disease.

opencc-zeroJul 2024View details →

opencc-by-4.0Jul 2024View details →

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases

Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we report a machine- learning framework, denoted ‘Tissue Risk Assessment of Causality by Expression for variants’ (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/), that offers two advancements. First, TRACEvar predicts pathogenic variants that disrupt the normal physiology of specific tissues. This was achieved by creating 14 tissue-specific models that were trained on over 14,000 variants and combined 84 attributes of genetic variants with 495 attributes derived from tissue omics. TRACEvar outperformed 10 well-established and tissue-oblivious variant effect prediction tools. Second, the resulting models are interpretable, thereby illuminating variants' mode-of-action. Application of TRACEvar to variants of 52 rare-disease patients highlighted pathogenicity mechanisms and relevant disease processes. Lastly, interpretation of large-scale models revealed that top-ranking determinants of pathogenicity included attributes of disease-affected tissues, particularly cellular process activities. Hence, tissue contexts and interpretable machine-learning models can greatly enhance the etiology of rare diseases. Article link: https://www.embopress.org/doi/full/10.1038/s44320-024-00061-6

Figure 3 in Severe glomerular disease in juvenile grey snapper Lutjanus griseus L. in the Gulf of Mexico caused by the myxozoan Sphaerospora motemarini n. sp.

Figure 3. SSU rDNA-based maximum likelihood (GTR + Γ model) tree of 26 myxosporean sequences showing the phylogenetic position of Sphaerospora motemarini n. sp. amongst all other polysporoplasmid Sphaerospora spp. sequenced to date (red) and within the Sphaerospora sensu stricto clade (yellow). Members of marine (blue) and freshwater (green) myxosporean lineages were used as outgroups. Maximum likelihood bootstraps/maximum parsimony bootstraps/Bayesian posterior probabilities shown at nodes. Dashes indicate bootstrap values <50 or not present in the maximum parsimony or Bayesian tree.

Figure 2 in Severe glomerular disease in juvenile grey snapper Lutjanus griseus L. in the Gulf of Mexico caused by the myxozoan Sphaerospora motemarini n. sp.

Figure 2. Line drawing of spore of Sphaerospora motemarini n. sp. summarizing all morphological details in two dimensions; spore with bilateral symmetry, labels given only on one side: SV = spore valve, VN = nucleus of valve cell, OP = ornamental surface pits on posterior part of spore valve, PC = polar capsule containing coiled polar filament (PF), PN = nucleus of polar capsule, SP = uninucleated sporoplasms (6); bar = 5 µm.

Figure 1 in Severe glomerular disease in juvenile grey snapper Lutjanus griseus L. in the Gulf of Mexico caused by the myxozoan Sphaerospora motemarini n. sp.

Figure 1. Morphology and histopathology of Sphaerospora motemarini n. sp. in grey snapper, Lutjanus griseus. (A) Mature spore in fresh kidney smear showing thickened suture at apical part (transparent arrowhead), two prominent bulges at posterior part (black arrowheads) and valve surface ornamentation (detailed in D) at the posteriolateral part of the spore; bar = 10 µm. (B) DAPI nuclear staining showing polysporoplasmic nature of spore (4 out of 6 sporoplasms visible in plane of image); bar = 10 µm. (C) Pseudoplasmodium containing two croissant-shaped sporoplasms (only visible around left spore) and two spores; bar = 10 µm. (D) SEM showing spore surface ornamentation in the shape of pits providing an opening to a multilayered canal system; bar = 5 µm. (E) Fresh smear of kidney showing early plasmodial stages (arrowheads) in a glomerulus; bar = 20 µm. (F–H) Histological sections stained with H&E; bar = 50 µm (F). Histopathological changes showing massive enlargement of renal corpuscles, with arrows in small insert (bottom left) indicating uninfected corpuscles of normal size in an uninfected kidney; bar = 50 µm. (G) Thickening of Bowman's capsule (arrows) and proliferation of mesangial cells; bar = 50 µm. (H) Engulfment of a spore by a melanin-rich macrophage (brown color); bar = 10 µm.

Fig. 2 in Wildlife disease ecology in changing landscapes: Mesopredator release and toxoplasmosis

Fig. 2. Map of Tasmania showing blood collection sites for the three native carnivore species and the introduced feral cat. Places identified are those referred to in the text.

Fig. 1 in Wildlife disease ecology in changing landscapes: Mesopredator release and toxoplasmosis

Fig. 1. Map of Tasmania showing average cat densities from individual spotlighting districts over 8 years and blood collection sites for the Tasmanian pademelon. Positive T. gondii sites are those where at least one sample tested positive to IgG antibodies. Negative sites are those where no evidence of exposure to T. gondii was found in any sample.

Fig. 3 in Wildlife disease ecology in changing landscapes: Mesopredator release and toxoplasmosis

Fig. 3. Prevalence of IgG antibodies of Tasmanian mammals to T. gondii by trophic level; n represents the total number of samples tested. Standard error bars are shown.

Fig. 5 in Beyond the disease: Is Toxoplasma gondii infection causing population declines in the eastern quoll (Dasyurus viverrinus)?

Fig. 5. Kaplan Meier survival curves comparing survival trajectories for seronegative (solid line) and seropositive (broken line) eastern quolls. Curves show survival of all quolls first captured at the non-declining site between August 2010 and October 2012.

Fig. 4 in Beyond the disease: Is Toxoplasma gondii infection causing population declines in the eastern quoll (Dasyurus viverrinus)?

Fig. 4. Association between seroprevalence of T. gondii IgG antibodies in eastern quolls at Cradle Mountain and the square-root transformed number of eastern quolls captured 2 months later (y = 2.552–0.022×). Each data point represents a single trapping/sampling session between May 2011 and July 2013.

Fig. 3 in Beyond the disease: Is Toxoplasma gondii infection causing population declines in the eastern quoll (Dasyurus viverrinus)?

Fig. 3. Seroprevalence of T. gondii IgG antibodies in juvenile eastern quolls from time of emergence for 2011 cohort ((a) declining sites (Cradoc and Judbury) and (b) nondeclining site (Bruny Island)) and 2012 cohort ((c) declining sites and (d) non-declining site). Declining sites were not surveyed in September 2012, November 2012, March 2013 or September 2013. Vertical axis shows proportion of quolls tested that were seropositive at titres À 64. Error bars represent 95% confidence intervals calculated using the Jeffreys interval estimation for a small sample size with binomial distribution (Brown et al., 2001).

Fig. 2 in Beyond the disease: Is Toxoplasma gondii infection causing population declines in the eastern quoll (Dasyurus viverrinus)?

Fig. 2. Seroprevalence of T. gondii IgG antibodies in adult eastern quolls at (a) declining sites (Cradoc and Judbury) and (b) non-declining site (Bruny Island). Declining sites were not surveyed in September or November 2012 or March 2013. Vertical axis shows proportion of quolls tested that were seropositive at titres À 64. Error bars represent 95% confidence intervals calculated using the Jeffreys interval estimation for a small sample size with binomial distribution (Brown et al., 2001).

Fig. 6 in Beyond the disease: Is Toxoplasma gondii infection causing population declines in the eastern quoll (Dasyurus viverrinus)?

Fig. 6. Comparison of the probability of T. gondii infection with quoll age, by site. Non-declining site = Bruny Island, Cradle Mountain = fluctuating site, Declining sites = pooled data from Cradoc and Judbury sites. Circles represent individual observations of seronegative (probability = 0) and seropositive (probability = 1) quolls at a given age, with darker circles indicating a higher number of quolls with the same combination of age and serological status. Curve illustrates the fitted data, with grey shading representing 95% confidence intervals.

Fig. 6 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect

Fig. 6. Podolica cattle in the Gallipoli Cognato Regional Park, Basilicata, southern Italy. These cattle move freely within the park's territory, helping in disseminating Ixodes ricinus to different altitudes (from 200 m to over 1000 m).

Fig. 5. A in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect

Fig. 5. A male of the winter tick Haemaphysalis inermis collected in a cold winter day in January 2010 in Basilicata, southern Italy.

Fig. 4 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect

Fig. 4. Shanghai, China: the largest city proper by population in the world. China is the world's largest carbon emitter; it accounted for 29% of global total emissions in 2012 (Olivier et al., 2013).

Fig. 3 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect

Fig. 3. Deforestation of Atlantic rainforest for the establishment of banana tree plantations in Amaraji, north-eastern Brazil.

Fig. 1 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect

Fig. 1. Climate change is contributing to sea level rise. The Boa Viagem beach is a tourist destination in Recife, north-eastern Brazil. If current trends in sea level rise persist, cities like Recife may be literally swallowed the sea in the coming decades.

Fig. 2 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect

Fig. 2. Sloth found on a road that crosses a region of Atlantic rainforest in Aldeia, north-eastern Brazil. Crab-eating foxes (Cerdocyon thous) and other wild animals are commonly seen crossing this road and are frequently victims of car crashes.