Common Cluster Research Articles

Long-Term Natural History of Autoimmune Gastritis: Results From a Prospective Monocentric Series.

The natural history of autoimmune gastritis (AIG) has been poorly described. In this study, we report the long-term natural history and clinical clustering of the full spectrum of AIG, from the potential to the complicated stage. Prospective single-center study conducted in a tertiary referral center. Patients with AIG at any stage (0 = potential; 1 = early; 2 = florid; 3 = severe; and 4 = complicated) were enrolled (January 2000-December 2022). The histopathological evolution, the clinical presentation, and the correlates of evolution of potential AIG were assessed. Four hundred ninety-eight patients with AIG (mean age 56.7 ± 15.2 years, F:M ratio 2.5:1) were included, of whom 93 experienced potential AIG. The maximum disease duration was 27 years (median 18, interquartile range 14-23), while the overall median follow-up was 52 months (interquartile range 12-95). Age was significantly lower in stage 0 compared with that in the other stages. Accidental histologic evidence and hematologic findings were the most common clusters of diagnosis. The overall median rate of progression was 7.29 per 100 persons/yr (95% confidence interval [CI] 6.19-8.59), while the stage-specific rates of progression were 10.85 (stage 0; 95% CI 7.75-15.18), 14.83 (stages 1-2; 95% CI 11.89-18.49), and 2.68 (stage 3; 95% CI 1.88-3.84). Newly onset neoplastic complications at follow-up occurred in 41/483 patients (8.5%; 23 neuroendocrine tumors and 18 epithelial dysplasia). No cases of adenocarcinoma were noticed. Male sex was associated with a greater likelihood of evolving from potential AIG to overt AIG. AIG is a progressive disorder, with a virtually absent risk of gastric adenocarcinoma. Patients with potential AIG should be monitored because they carry a high risk of evolving into overt AIG.

Clinical characteristics, allergic response to autologous semen, and desensitization in patients with postorgasmic illness syndrome.

Postorgasmic illness syndrome (POIS) is rare and includes a cluster of physical and cognitive symptoms that occur after ejaculation. The pathogenesis and effective treatments remain unclear. This study aimed to characterize the symptomatology of POIS, study the allergic response of autologous semen in patients and controls, and evaluate the effects of desensitization therapy. The clinical characteristics of 24 Chinese patients with POIS were analyzed. Skin prick tests, intracutaneous tests, and specific IgE detection were performed with autologous semen. Five patients were desensitized via subcutaneous injections of autologous semen. Evaluated outcomes included the clinical features of POIS; scores of the Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS), and visual analog scale (VAS) of symptoms; skin reactions; desensitization with diluted autologous seminal fluid; and the IgE reactivity patterns of immunoblotting and enzyme-linked immunosorbent assay in vitro. The most common symptom cluster was the general cluster, and the most prevalent symptoms were extreme fatigue and inattention. A total of 66.67% (14/21) of the patients had no symptoms or milder symptoms after nocturnal emission than after intercourse or masturbation. Of the patients, 87.5% (21/24) had psychiatric symptoms and 53.85% (7/13) had abnormal sex hormone levels. The SAS and SDS scores of the high and low VAS groups were significantly higher than those of the control group. Pearson analysis showed that the correlation coefficient between the SAS and VAS was 0.607 (P < .01) and that between the SDS and VAS was 0.490 (P < .05). The patients and healthy donors all had positive intracutaneous test results with their own semen, negative skin prick test results, and no IgE specific to autologous semen. Most patients (4/5) did not achieve ideal therapeutic effects with desensitization. Allergy is not the main pathogenesis of POIS, and desensitization with autologous semen is not effective for most patients. This project included the largest number of patients with POIS in China and assessed the allergic response to autologous semen and the effect of desensitization therapy. There is no objective method for evaluating the efficacy of desensitization with autologous semen. IgE-mediated semen allergy is not the main pathogenesis of POIS, and there is a positive chance that POIS is related to psychological factors. Most patients do not respond to desensitization with autologous semen, and POIS treatment should be individualized, especially in cases with uncertain causes.

A heterogeneous parallel model of unstructured mesh finite element method based on CPU+GPU

Most of the existing numerical simulation programs using the unstructured mesh finite element are based on the traditional multicore processor architecture. With the increase of the number of computing meshes, the computing time is increasing, which leads to the common multicore CPU cluster can’t meet the high computing demand of complex applications. In order to adapt to the trend of the heterogeneous development of high-performance computers, a heterogeneous parallel model of unstructured mesh finite element method is proposed in this paper. It can transplant the unstructured mesh finite element program framework to heterogeneous platform better and faster. The model realizes the efficient utilization of the multicore CPU by hierarchical parallelization, and realizes the efficient utilization of GPU by heterogeneous parallel rewriting for time-consuming computing hotspot. Finally, the model is applied to the parallel transplantation of CPU + GPU heterogeneous platform for the thermal radiation effect program. The results show that the model can reduce the programming difficulty and has good portability and extensibility.

Enhancing histopathological image classification of invasive ductal carcinoma using hybrid harmonization techniques

This study aims to develop a robust pipeline for classifying invasive ductal carcinomas and benign tumors in histopathological images, addressing variability within and between centers. We specifically tackle the challenge of detecting atypical data and variability between common clusters within the same database. Our feature engineering-based pipeline comprises a feature extraction step, followed by multiple harmonization techniques to rectify intra- and inter-center batch effects resulting from image acquisition variability and diverse patient clinical characteristics. These harmonization steps facilitate the construction of more robust and efficient models. We assess the proposed pipeline’s performance on two public breast cancer databases, BreaKHIS and IDCDB, utilizing recall, precision, and accuracy metrics. Our pipeline outperforms recent models, achieving 90-95% accuracy in classifying benign and malignant tumors. We demonstrate the advantage of harmonization for classifying patches from different databases. Our top model scored 94.7% for IDCDB and 95.2% for BreaKHis, surpassing existing feature engineering-based models (92.1% for IDCDB and 87.7% for BreaKHIS) and attaining comparable performance to deep learning models. The proposed feature-engineering-based pipeline effectively classifies malignant and benign tumors while addressing variability within and between centers through the incorporation of various harmonization techniques. Our findings reveal that harmonizing variabilities between patches from different batches directly impacts the learning and testing performance of classification models. This pipeline has the potential to enhance breast cancer diagnosis and treatment and may be applicable to other diseases.

Seroprevalence and Genotypic Characterization of Orientia tsutsugamushi in Febrile Pediatric Patients Admitted in Tertiary Care Hospital of Chennai, South India

Scrub typhus is one of the important vector borne illness which is largely underdiagnosed, particularly in children. It causes mild febrile illness to severe complications. More than 20 genotypic clusters are documented from various geographical regions based on sequence variations of 56kDa type specific antigen gene of Orientia tsutsugamushi, the causative agent of scrub typhus. Adequate knowledge about epidemiology and genetic diversity in endemic regions is an important tool for clinical management, development of diagnostic kit and vaccines. Limited studies are available based on genotypic characterization of Orientia tsutsugamushi in children. The present study determined the prevalence and genotypic characterization of Orientia tsutsugamushi in febrile pediatric patients admitted in tertiary care hospital of Chennai, South India. Both serum and blood samples were collected from 239 scrub typhus suspected febrile pediatric patient’s aged between 6 months to 12 years. IgM ELISA and 56kDa nested PCR were performed on all the patient samples. Nested PCR positive samples were sequenced and analyzed for genotypic differences. Among 239 samples, 103 were positive for IgM ELISA and 35 were positive for nPCR analysis. Out of the 108 scrub typhus positive cases, 45.31% (58/128) were male and 45.05% (50/111) were female. Eschar was positive in 56.48% of patients. Pneumonia (4/108), hypotensive shock (3/108), and myocarditis (1/108) were the most common clinical complications associated with scrub typhus positive children. Karp (56.6%) was the most common genotypic cluster found in our study, followed by TA716 (33.33%), TA763 (2/30), and Gilliam (1/30).

Multi-faceted analysis and prediction for the outbreak of pediatric respiratory syncytial virus.

Respiratory syncytial virus (RSV) is a significant cause of pediatric hospitalizations. This article aims to utilize multisource data and leverage the tensor methods to uncover distinct RSV geographic clusters and develop an accurate RSV prediction model for future seasons. This study utilizes 5-year RSV data from sources, including medical claims, CDC surveillance data, and Google search trends. We conduct spatiotemporal tensor analysis and prediction for pediatric RSV in the United States by designing (i) a nonnegative tensor factorization model for pediatric RSV diseases and location clustering; (ii) and a recurrent neural network tensor regression model for county-level trend prediction using the disease and location features. We identify a clustering hierarchy of pediatric diseases: Three common geographic clusters of RSV outbreaks were identified from independent sources, showing an annual RSV trend shifting across different US regions, from the South and Southeast regions to the Central and Northeast regions and then to the West and Northwest regions, while precipitation and temperature were found as correlative factors with the coefficient of determination R2≈0.5, respectively. Our regression model accurately predicted the 2022-2023 RSV season at the county level, achieving R2≈0.3 mean absolute error MAE < 0.4 and a Pearson correlation greater than 0.75, which significantly outperforms the baselines with P-values <.05. Our proposed framework provides a thorough analysis of RSV disease in the United States, which enables healthcare providers to better prepare for potential outbreaks, anticipate increased demand for services and supplies, and save more lives with timely interventions.

Dating common commodity price and inflation shocks with alternative approaches

This paper investigates the occurrence of common price shocks (co-exceedance) across different commodities. IMF monthly price series of 11 commodities are considered over the 1980-2021 period. The analysis considers two alternative stochastic processes. The first looks for common volatility clusters using individual GARCH models to detect whether and when respective clusters overlap. Through an appropriate battery of tests, the second alternative looks for a common Bubble Generating Process (BGP) by searching for individual explosive roots and then dating them to identify the possible overlaps and first movers. Evidence emerging about these shock generating processes is linked to the analogous behaviour of the US Consumer Price Index (CPI) to assess to what extent inflation shocks can be associated to the observed commodity price spikes. Results show that the detection of temporary bubbles and volatility clusters only partially agrees on the episodes of exuberance, on the first-moving commodities and on the involvement of the CPI. This provides helpful suggestions on the development of a real-time surveillance tool supporting policy intervention in periods of commodity price turbulence.

ASSESSMENT OF THE EFFECT OF DIFFERENT CLUSTERS OF THE METABOLIC SYNDROME ON THE EXPRESSION OF DEPRESSIVE DISORDERS

The purpose of the study. To study the influence of various metabolic syndrome clusters on the severity of depressive disorders according to the CES-D scale.&#x0D; Materials and methods. 136 male patients were examined, including 60 with metabolic syndrome. Metabolic syndrome was diagnosed based on the criteria of clinical recommendations of the All-Russian Scientific Society of Cardiology in 2009. The severity of depression was assessed using the Center for Epidemiological Studies Depression Scale - CES-D.&#x0D; Results. It was found that among the surveyed men with metabolic syndrome, depressive disorders of varying severity on the CES-D scale were found in 40% of the surveyed, while among men with MS, the number of people with severe depression on the CES-D scale was higher. Among persons with a four-component metabolic syndrome cluster, patients with severe depression were more common (57,1%). In men with depression on the CES-D scale of varying severity, a three-component cluster of metabolic syndromes was more common, consisting of abdominal obesity, arterial hypertension, and an elevated level of low-density lipoprotein cholesterol.&#x0D; Conclusion. The combination of abdominal obesity, arterial hypertension, and lipid disorders characterized by high levels of low-density lipoprotein was the most common metabolic syndrome cluster among men with depressive disorders of varying severity on the CES-D scale.

Postorgasmic illness syndrome: one or several entities? A retrospective cohort study.

Postorgasmic illness syndrome (POIS) is a rare syndrome in which patients experience various physical and cognitive symptoms after ejaculation, and its pathophysiology remains unknown. The aim of this study was to characterize the clinical presentations, disease course, and treatment outcomes in outpatients, as well as to examine the validity of the preliminary criteria and symptom clusters of POIS. This retrospective monocentric study was conducted in a neurourology department, with patients included from 2010 to 2023. The diagnostic criteria and symptom cluster descriptions were based on previous studies. The study focused on the clinical features of POIS, the complementary tests performed, the treatments tried, and their effectiveness. Thirty-seven men were included in the study, with symptom onset occurring at a mean ± SD age of 23.6 ± 7.4years. The mean time from ejaculation to symptom onset was 1hour 22minutes ± 3hours 42minutes. The mean duration of symptoms was 4.7 ± 3.4days. Seventeen patients (46%) developed the symptoms primarily, whereas in 20 (54%) they appeared secondarily. All preliminary criteria were met in 19 patients (51%). The most common symptom clusters were "general" in all 37 patients (100%; eg, asthenia and concentration difficulties) and "head" in 35 patients (95%; mostly headache and a foggy feeling). In terms of treatments, antihistamines and nonsteroidal anti-inflammatory drugs were tried and partially improved symptoms for some patients. This study helps to further characterize POIS by specifying the most frequent symptoms and comparing them with the initial criteria. To our knowledge, this is one of the largest cohorts of patients consulting for symptoms suggestive of POIS. There are limitations due to the retrospective nature of the data collection, such as missing data and imprecision of treatment efficacy. The majority of participants met at least 3 of the preliminary diagnostic criteria, with a majority of symptoms in the general and head clusters. However, the determination of predictive factors for treatment response based on the typology of the disorders remains to be established.

Evaluation of historic and new detection algorithms for different types of plastics over land and water from hyperspectral data and imagery

Several spectral indices have been proposed in the last decade for remote detection of macroplastics in the environment, however no comprehensive analysis has been provided on the sensitivity of those algorithms to different plastic types and their application to remote imagery over land and water. In addition, algorithm threshold values for plastic detection are typically variable per scene, and evaluation of how thresholds perform in multiple images is scarce. In this study we use a reflectance database of diverse pure plastic and non-plastic materials to evaluate spectral signatures for algorithm design and specificity evaluation, as well as to define optical classes for plastics. Published and new algorithms proposed in this study were then evaluated on three flight lines, comprising different observation geometries, of the Airborne Prism Experiment (APEX) sensor taken over Ostend, Belgium. Our results show that most common plastics cluster into two different categories based on their absorption features in the near infrared (NIR) and shortwave infrared (SWIR): Type 1 includes polyethylene, polypropylene, and vinyl, while Type 2 includes polyester, polystyrene, and acrylic. Nylon (polyamide) forms a separate spectral signature. Most line height algorithms were specific in the sense of mostly detecting plastic of either Type 1 or Type 2, while the evaluated ratio algorithms provided less distinction. All but one of the evaluated algorithms were found to have a threshold value that provided distinction between plastic and non-plastic materials (e.g., wood, concrete, algae, ice, etc.). Only the thresholds of the newly developed algorithms were consistent between the reflectance database and the imagery. Detection of macroplastic between flight lines was more consistent over land than over water. Floating transparent plastic bottles (Type 2) with plastic labels (Type 1) and floating polyethylene bags (Type 1) were only detected in one of the three flight lines. Our results support previous observations on the importance of high spatial resolution to minimize mixed spectral signals within a pixel, and points to the relevance of the thickness of the plastic layer influencing the detection for a given background and mixed signal. We also show that residual errors from atmospheric correction have a higher impact on the performance of ratio indexes than line heights. Our research suggests that the different plastic-specific algorithms can be used as complimentary metrics for plastic remote sensing, potentially enabling plastic optical type classification.

Genetic characterization and phylogenetic analysis of human norovirus infection in individual municipalities of the Sverdlovsk region in 2022

Introduction. Human Noroviruses (HuNoV) are highly contagious pathogens responsible of acute human norovirus infection (HuNoVI). GII.4 is the prevailing norovirus genotype in the incidence of gastroenteritis worldwide. Currently there are no studies on the molecular monitoring and phylogenetic analysis of HuNoV in the territory of the Sverdlovsk region, therefore it is not possible to objectively assess their genetic diversity.&#x0D; The aim of the study is to carry out genotyping and phylogenetic analysis of HNoVI in municipalities of the Sverdlovsk region: Ekaterinburg, Nizhny Tagil, Kamensk-Uralsky in 2022.&#x0D; Materials and methods. Fecal samples were collected from children suffering from HuNoVI in the municipalities of the Sverdlovsk region. Viral RNA was extracted from stool samples followed by reverse transcription polymerase chain reaction with degenerate primers flanking the ORF1/ORF2 junction. The amplified samples were genotyped by Sanger sequencing. Multiple sequence alignments were carried out and phylogenetic trees were constructed.&#x0D; Results. The total number of studied samples was 220. According to sequencing results, 73 positive sequences of HuNoV were typed, which represents 33% of the total volume of the studied samples. The largest share of genotypes is occupied by noroviruses GII (58%), noroviruses GI (42%). Noroviruses GII.17, GI.3 were the dominant genotypes. Phylogenetic analysis demonstrates that the identified sequences on the territory of the Sverdlovsk region have the smallest genetic distance, which gives grounds for their unification into a common cluster.&#x0D; Conclusion. For the first time, a genetic analysis of HuNoV was carried out in the territory of the Sverdlovsk region. The norovirus genotyping system based on the amplification of the ORF1/ORF2 region makes it possible to successfully identify various genotypes of noroviruses from the Sverdlovsk region. It has been confirmed that GII noroviruses remain the dominant genetic group. The results of phylogenetic analysis demonstrate the greatest homology in the formed clusters between isolates from Ekaterinburg, Nizhny Tagil, Kamensk-Uralsky.

Identification of a common ice nucleus on hydrophilic and hydrophobic close-packed metal surfaces

Establishing a general model of heterogeneous ice nucleation has long been challenging because of the surface water structures found on different substrates. Identifying common water clusters, regardless of the underlying substrate, is one of the key steps toward solving this problem. Here, we demonstrate the presence of a common water cluster found on both hydrophilic Pt(111) and hydrophobic Cu(111) surfaces using scanning tunneling microscopy and non-contact atomic force microscopy. Water molecules self-assemble into a structure with a central flat-lying hexagon and three fused pentagonal rings, forming a cluster consisting of 15 individual water molecules. This cluster serves as a critical nucleus during ice nucleation on both surfaces: ice growth beyond this cluster bifurcates to form two-dimensional (three-dimensional) layers on hydrophilic (hydrophobic) surfaces. Our results reveal the inherent similarity and distinction at the initial stage of ice growth on hydrophilic and hydrophobic close-packed metal surfaces; thus, these observations provide initial evidence toward a general model for water-substrate interaction.

Deciphering Millet Diversity: Proteomic Clusters and Phylogenetic Insights

Deciphering Millet Diversity: Proteomic Clusters and Phylogenetic Insights

Identification and validation of core genes for type 2 diabetes mellitus by integrated analysis of single-cell and bulk RNA-sequencing

BackgroundThe exact mechanisms of type 2 diabetes mellitus (T2DM) remain largely unknown. We intended to authenticate critical genes linked to T2DM progression by tandem single-cell sequencing and general transcriptome sequencing data.MethodsT2DM single-cell RNA-sequencing data were submitted by the Gene Expression Omnibus (GEO) database and ArrayExpress (EBI), from which gene expression matrices were retrieved. The common cell clusters and representative marker genes were ascertained by principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), CellMarker, and FindMarkers in two datasets (GSE86469 and GSE81608). T2DM-related differentially expressed marker genes were defined by intersection analysis of marker genes and GSE86468-differentially expressed genes. Receiver operating characteristic (ROC) curves were utilized to assign representative marker genes with diagnostic values by GSE86468, GSE29226 and external validation GSE29221, and their prospective target compounds were forecasted by PubChem. Besides, the R package clusterProfiler-based functional annotation was designed to unveil the intrinsic mechanisms of the target genes. At last, western blot was used to validate the alternation of CDKN1C and DLK1 expression in primary pancreatic islet cells cultured with or without 30mM glucose.ResultsThree common cell clusters were authenticated in two independent T2DM single-cell sequencing data, covering neurons, epithelial cells, and smooth muscle cells. Functional ensemble analysis disclosed an intimate association of these cell clusters with peptide/insulin secretion and pancreatic development. Pseudo-temporal trajectory analysis indicated that almost all epithelial and smooth muscle cells were of neuron origin. We characterized CDKN1C and DLK1, which were notably upregulated in T2DM samples, with satisfactory availability in recognizing three representative marker genes in non-diabetic and T2DM samples, and they were also robustly interlinked with the clinical characteristics of patients. Western blot also demonstrated that, compared with control group, the expression of CDKN1C and DLK1 were increased in primary pancreatic islet cells cultured with 30 mM glucose for 48 h. Additionally, PubChem projected 11 and 21 potential compounds for CDKN1C and DLK1, respectively.ConclusionIt is desirable that the emergence of the 2 critical genes indicated (CDKN1C and DLK1) could be catalysts for the investigation of the mechanisms of T2DM progression and the exploitation of innovative therapies.

Genomic Epidemiology and Evolution of Fowl Adenovirus 1.

Fowl adenovirus 1 (FAdV-1) is the main cause of gizzard erosion in chickens. Whole genome sequencing and sequence analyses of 32 FAdV-1 strains from a global collection provided evidence that multiple recombination events have occurred along the entire genome. In gene-wise phylogenies, only the adenoviral pol gene formed a tree topology that corresponded to whole genome-based phylogeny. Virus genetic features that were clearly connected to gizzard erosion were not identified in our analyses. However, some genome variants tended to be more frequently identified from birds with gizzard erosion and strains isolated from healthy birds or birds with non-specific pathologies tended to form common clusters in multiple gene phylogenies. Our data show that the genetic diversity is greater, and the evolutionary mechanisms are more complex within FAdV-1 than previously thought. The implications of these findings for viral pathogenesis and epidemiology await further investigation.

A Three-Dimensional Hail Trajectory Clustering Technique

Abstract Recent advances in hail trajectory modeling regularly produce datasets containing millions of hail trajectories. Because hail growth within a storm cannot be entirely separated from the structure of the trajectories producing it, a method to condense the multidimensionality of the trajectory information into a discrete number of features analyzable by humans is necessary. This article presents a three-dimensional trajectory clustering technique that is designed to group trajectories that have similar updraft-relative structures and orientations. The new technique is an application of a two-dimensional method common in the data mining field. Hail trajectories (or “parent” trajectories) are partitioned into segments before they are clustered using a modified version of the density-based spatial applications with noise (DBSCAN) method. Parent trajectories with segments that are members of at least two common clusters are then grouped into parent trajectory clusters before output. This multistep method has several advantages. Hail trajectories with structural similarities along only portions of their length, e.g., sourced from different locations around the updraft before converging to a common pathway, can still be grouped. However, the physical information inherent in the full length of the trajectory is retained, unlike methods that cluster trajectory segments alone. The conversion of trajectories to an updraft-relative space also allows trajectories separated in time to be clustered. Once the final output trajectory clusters are identified, a method for calculating a representative trajectory for each cluster is proposed. Cluster distributions of hailstone and environmental characteristics at each time step in the representative trajectory can also be calculated. Significance Statement To understand how a storm produces large hail, we need to understand the paths that hailstones take in a storm when growing. We can simulate these paths using computer models. However, the millions of hailstones in a simulated storm create millions of paths, which is hard to analyze. This article describes a machine learning method that groups together hailstone paths based on how similar their three-dimensional structures look. It will let hail scientists analyze hailstone pathways in storms more easily, and therefore better understand how hail growth happens.

FEATURES OF INDICATORS OF SITUATIONAL AND PERSONAL ANXIETY IN PATIENTS WITH METABOLIC SYNDROME

The purpose of the study. To study the influence of various metabolic syndrome clusters on the severity of situational and personal anxiety.&#x0D; Materials and methods. The study included 136 patients, including 60 with metabolic syndrome. Metabolic syndrome was diagnosed based on the criteria of clinical recommendations of the All-Russian Scientific Society of Cardiology in 2009. The severity of personal and situational anxiety was assessed using the scale of self-assessment of the level of anxiety by Ch.D. Spielberger modified by Yu.L. Khanina.&#x0D; Results. It was established that in the examined groups of men, the median scores of both personal and situational anxieties corresponded to a moderate level. The most common variant of metabolic syndrome among men with high and moderate levels of situational and personal anxiety was its three-component combination. Among men with moderate and high levels of situational anxiety, there were statistically significantly more people with 5 or more metabolic syndrome components compared to patients with low levels.&#x0D; Conclusion. The combination of central obesity, arterial hypertension, and lipid disorders characterized by high levels of low-density lipoprotein was the most common cluster of MS among men with high and moderate levels of situational and personal anxiety.

In-silico identification and characterization of lipase gene sequences present in hexaploid wheat

Lipases (EC number 3.1.1.3), capable of hydrolyzing triacylglycerols (TAGs) into glycerol and fatty acids, gained attention due to diverse biotechnological applications and are extensively used in industries like dairy, pharmaceutical, cosmetic, food etc. Industrial use of microbial lipases is very costly, so an alternative seed lipase is used due to its low cost, specificity and ease of purification. Lipolytic activities in wheat (genus Triticum) are shown by embryo and aleurone lipids. The present study was undertaken to characterize the identified wheat lipase sequences with the help of bioinformatics tools. 63 wheat lipase genes were identified of which two genes matched common clusters with known lipase sequences of Arabidopsis, Oryza, and Zea mays through phylogenetic tree analysis. From STRING database (v11.0) analysis only one gene (ID: 000056_1AL) showed perfect 1st interaction networking. It depicted 8 functionally transcript partners with 9 nodes and 17 edges of which only 6 nodes have shown 2nd interaction. On further analysis, sequence 000056_1AL showed optimum physico-chemical characters with catalytic activity in lipid metabolism and a better network with 8 functionally associated transcript proteins. Domains present in the sequence 000056_1AL are TAG lipase, lipase class - 3, and phospholipase A1, which show broad substrate specificity of the particular protein.

Liver metastases across cancer types sharing tumor environment immunotolerance can impede immune response therapy and immune monitoring

BackgroundHepatic immune tolerance might contribute to the development of therapeutic resistance to immunotherapy. However, addressing this issue is challenging since the efficacy of immunotherapy in the context of liver metastasis (LM) remains poorly studied. Here, we aimed to establish an LM common immune feature (LMCIF) score to quantify the characteristics of LM immunotolerance across cancer types for assisting clinical disease management. MethodsLarge-scale clinical data were collected to identify the prognosis of LM. Multi-omics datasets of metastatic cancers with LM special immune-related pathways (LMSIPs) from the Molecular Signatures Database (MSigDB)were used to obtain an LMCIF cluster. Based on differential expression genes (DEGs), a novel LMCIF score for the LMCIF cluster was constructed. In addition, multi-omics, and immunohistochemistry (IHC) data from the public and in-house cohorts were used to explore the features of LM, and LMCIF score. ResultsPatients with LM had a worse prognosis and significantly lower infiltration of immune cells than patients with metastasis to other organs when analyzed with combined clinical and RNA sequencing data. After extracting the LMCIF cluster from 373 samples by utilizing 29 LMSIPs and validating them in a microarray cohort, an LMCIF score was established to confirm the role of the immunosuppressive environment as a contributor to the poor prognosis of LM across cancer types. Moreover, this LMCIF score could be used to predict the immune response of cancer patients undergoing immunotherapy. Finally, we identified that the majority of the 31 LMCIF genes exhibited a negative correlation with TME cells in LM patients, one of them, KRT19, which possessed the strongest positive correlation with LMCIF score, was confirmed to have an immunosuppressive effect through IHC analysis. ConclusionsOur results suggest that LM across cancer types share similar immunological profiles that induce immunotolerance and escape from immune monitoring. The novel LMCIF score represents a common liver metastasis immune cluster for predicting immunotherapy response, the results of which might benefit clinical disease management.

Comparative genomics of bacteria from amphibian skin associated with inhibition of an amphibian fungal pathogen, Batrachochytrium dendrobatidis.

Chytridiomycosis, caused by Batrachochytrium dendrobatidis (Bd), is a skin disease associated with worldwide amphibian declines. Symbiotic microbes living on amphibian skin interact with Bd and may alter infection outcomes. We completed whole genome sequencing of 40 bacterial isolates cultured from the skin of four amphibian species in the Eastern US. Each isolate was tested in vitro for the ability to inhibit Bd growth. The aim of this study was to identify genomic differences among the isolates and generate hypotheses about the genomic underpinnings of Bd growth inhibition. We identified sixty-five gene families that were present in all 40 isolates. Screening for common biosynthetic gene clusters revealed that this set of isolates contained a wide variety of clusters; the two most abundant clusters with potential antifungal activity were siderophores (N=17 isolates) and Type III polyketide synthases (N=22 isolates). We then examined various subsets of the 22 isolates in the phylum Proteobacteria for genes encoding specific compounds that may inhibit fungal growth, including chitinase and violacein. We identified differences in Agrobacterium and Sphingomonas isolates in the chitinase genes that showed some association with anti-Bd activity, as well as variation in the violacein genes in the Janthinobacterium isolates. Using a comparative genomics approach, we generated several testable hypotheses about differences among bacterial isolates from amphibian skin communities that could contribute to variation in the ability to inhibit Bd growth. Further work is necessary to explore and uncover the various mechanisms utilized by amphibian skin bacterial isolates to inhibit Bd.