Biofilm marker discovery with cloud-based dockerized metagenomics analysis of microbial communities.

In an environment, microbes often work in communities to achieve most of their essential functions, including the production of essential nutrients. Microbial biofilms are communities of microbes that attach to a nonliving or living surface by embedding themselves into a self-secreted matrix of extracellular polymeric substances. These communities work together to enhance their colonization of surfaces, produce essential nutrients, and achieve their essential functions for growth and survival. They often consist of diverse microbes including bacteria, viruses, and fungi. Biofilms play a critical role in influencing plant phenotypes and human microbial infections. Understanding how these biofilms impact plant health, human health, and the environment is important for analyzing genotype-phenotype-driven rule-of-life functions. Such fundamental knowledge can be used to precisely control the growth of biofilms on a given surface. Metagenomics is a powerful tool for analyzing biofilm genomes through function-based gene and protein sequence identification (functional metagenomics) and sequence-based function identification (sequence metagenomics). Metagenomic sequencing enables a comprehensive sampling of all genes in all organisms present within a biofilm sample. However, the complexity of biofilm metagenomic study warrants the increasing need to follow the Findability, Accessibility, Interoperability, and Reusable (FAIR) Guiding Principles for scientific data management. This will ensure that scientific findings can be more easily validated by the research community. This study proposes a dockerized, self-learning bioinformatics workflow to increase the community adoption of metagenomics toolkits in a metagenomics and meta-transcriptomics investigation. Our biofilm metagenomics workflow self-learning module includes integrated learning resources with an interactive dockerized workflow. This module will allow learners to analyze resources that are beneficial for aggregating knowledge about biofilm marker genes, proteins, and metabolic pathways as they define the composition of specific microbial communities. Cloud and dockerized technology can allow novice learners-even those with minimal knowledge in computer science-to use complicated bioinformatics tools. Our cloud-based, dockerized workflow splits biofilm microbiome metagenomics analyses into four easy-to-follow submodules. A variety of tools are built into each submodule. As students navigate these submodules, they learn about each tool used to accomplish the task. The downstream analysis is conducted using processed data obtained from online resources or raw data processed via Nextflow pipelines. This analysis takes place within Vertex AI's Jupyter notebook instance with R and Python kernels. Subsequently, results are stored and visualized in Google Cloud storage buckets, alleviating the computational burden on local resources. The result is a comprehensive tutorial that guides bioinformaticians of any skill level through the entire workflow. It enables them to comprehend and implement the necessary processes involved in this integrated workflow from start to finish. This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

CREID - A ChemoReceptor-Effector Interaction Database.

The ChemoReceptor-Effector Interaction Database (CREID) is a collection of bacterial chemoreceptor and effector protein and interaction data to understand the process that chemoreceptors and effectors play in various environments. Our website includes terms associated with chemosensory pathways to educate users and those involved in collaborative research to help them understand this complex biological network. It includes 2,440 proteins involved in chemoreceptor and effector systems from 7 different bacterial families with 1,996 chemoeffector interactions. It is available at https://reactcreid.bicbioeng.org.

Transmesenteric hernia with closed-loop small bowel obstruction: A case report.

BACKGROUND: Transmesenteric hernia is a subtype of internal abdominal hernia (IAH) and a rare cause of small bowel obstruction in adults. Difficulty in reaching a definitive diagnosis due to non-specific clinical and imaging findings often cause life-threatening bowel ischemia. CASE REPORT: We report a case of a 37-year-old female who presented with clinical and imaging features of small bowel obstruction. She underwent an emergency laparotomy where the diagnosis of transmesenteric hernia causing closed-loop obstruction was made. The non-viable portion of the intestine was resected, anastomosis of the ileum along with the closure of the mesenteric defect was performed. DISCUSSION: IAH is the protrusion of abdominal viscera, most commonly small bowel loops through a peritoneal or mesenteric defect into the abdominal or pelvic cavity. Considered common in children, it is rare in adults and is most common after abdominal surgeries like Roux-en-Y gastric bypass surgery. Clinical features and imaging findings are non-specific causing delay in the diagnosis. CONCLUSION: A high index of suspicion is required while assessing the patient with symptoms suggestive of acute bowel obstruction as the preoperative diagnosis of a transmesenteric hernia is challenging.

Deep learning strategies for addressing issues with small datasets in 2D materials research: Microbial Corrosion.

Protective coatings based on two dimensional materials such as graphene have gained traction for diverse applications. Their impermeability, inertness, excellent bonding with metals, and amenability to functionalization renders them as promising coatings for both abiotic and microbiologically influenced corrosion (MIC). Owing to the success of graphene coatings, the whole family of 2D materials, including hexagonal boron nitride and molybdenum disulphide are being screened to obtain other promising coatings. AI-based data-driven models can accelerate virtual screening of 2D coatings with desirable physical and chemical properties. However, lack of large experimental datasets renders training of classifiers difficult and often results in over-fitting. Generate large datasets for MIC resistance of 2D coatings is both complex and laborious. Deep learning data augmentation methods can alleviate this issue by generating synthetic electrochemical data that resembles the training data classes. Here, we investigated two different deep generative models, namely variation autoencoder (VAE) and generative adversarial network (GAN) for generating synthetic data for expanding small experimental datasets. Our model experimental system included few layered graphene over copper surfaces. The synthetic data generated using GAN displayed a greater neural network system performance (83-85% accuracy) than VAE generated synthetic data (78-80% accuracy). However, VAE data performed better (90% accuracy) than GAN data (84%-85% accuracy) when using XGBoost. Finally, we show that synthetic data based on VAE and GAN models can drive machine learning models for developing MIC resistant 2D coatings.

Fecal Carriage of Extended-Spectrum beta-Lactamase Producing Escherichia coli among Health Science Students.

BACKGROUND: The incidence of antibiotic resistance in commensal bacteria is increasing with the production of extended-spectrum beta-lactamase. Therefore, this study was conducted to understand the status of fecal carriage of such enzyme producing Escherichia coli among health science students of seven different faculties of Institute of Medicine, Tribhuvan University. METHODS: This was a cross-sectional study conducted over six months among the health science students. One stool sample collected from each student was cultured and Escherichia coli isolates were identified, antibiotic sensitivity profile was produced, and extended-spectrum beta-lactamase production was detected following Clinical and Laboratory Standards Institute guidelines. RESULTS: A total of 156 students participated in the study, and Escherichia coli was isolated from all. Out of the total 156 Escherichia coli isolates, 11.5% were extended-spectrum beta-lactamase-producers and 14.7% were multidrug-resistant. The highest rate of fecal carriage of extended-spectrum beta-lactamase-producing Escherichia coli was found among Bachelor of Medicine and Bachelor of Surgery students (17.5%) and Bachelor of Science in Medical Imaging Technology (16.7%) students. Such enzyme producing Escherichia coli was found in the range of 6.9% to 25.0% among second- to fifth-year students. A significant number of extended-spectrum beta-lactamase-producing isolates were resistant to ciprofloxacin and gentamicin, apart from other extended-spectrum beta-lactamase substrate antibiotics, when compared with non-producers. CONCLUSIONS: A high rate of extended-spectrum beta-lactamase-producing Escherichia coli was detected from the gut of healthy health science students which indicates their possible dissemination throughout the wider community resulting in potential outbreak of infections caused by such organisms.

Giant ovarian mucinous cystadenocarcinoma: A case report.

Giant ovarian tumors are rare in the present day due to the early diagnosis and treatment. However, owing to the large size, it can often compress the inferior vena cava and sudden decompression of it during the removal can lead to hemodynamic instability with disastrous outcomes.

Prevalence and Contributing Factors of Gender-based Violence in SAARC Territories from 2010 to 2020: A Systematic Review and Meta-analysis.

BACKGROUND: Gender-based violence is a key global concern due to the high prevalence and increased socio-economic burden for survivors. However, estimation of the prevalence of gender-based violence is difficult due to differences in study design and underreporting of abuse, especially in developing nations. Therefore, we conducted this study to estimate the prevalence of Gender-based violence among women living in the SAARC region. METHODS: The review protocol was registered in PROSPERO (CRD42020219577). Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed throughout the review. A thorough database search was conducted to identify studies done in the SAARC region. Title and abstract screening were done in Covidence, followed by a full-text review. Data were extracted and pooled for analysis using the inclusion and exclusion criteria. Subgroup analysis was done where possible. RESULTS: A total of 76 studies were included in the systematic review and metaanalysis. The community prevalence of domestic violence (DV) was 43.8% (95% CI, 35.1% - 52.9%), GBV prevalence was 34.9% (95% CI, 30.2% - 39.9%) and IPV prevalence was 39.8% (95% CI, 30.7% - 49.6%). GBV prevalence was highest in illiterate women [54.2% (95% CI, 46.8% - 61.5%)] and lowest among women with higher than secondary level education [23.1% (95% CI, 16.2% - 32.0%)]. The prevalence of GBV among women in pregnancy or postpartum period was 32.3% (95% CI, 25.1% - 40.4%, I2: 98.64), while among female sexual workers, the prevalence of Gender-based violence was 42.1% (95% CI, 28.1% - 57.5%, I2: 99.25). CONCLUSIONS: There is a high prevalence of Gender-based violence in the SAARC region. Higher socioeconomic status and educational status are protective factors for Gender-based violence. However, more studies using validated tools are needed to understand the true extent of the problem.

Tubal abortion masquerading as an acute appendicitis with a negative urine pregnancy test: A case report.

INTRODUCTION AND IMPORTANCE: Spontaneous expulsion of product of conception through the fimbrial end to the peritoneal cavity is a rare mode of progression of tubal pregnancy. Thus, ectopic pregnancy can present with right-sided iliac fossa pain which can be preoperatively misdiagnosed as acute appendicitis. CASE HISTORY: A 30-year regularly menstruating woman presented with right iliac fossa pain which was diagnosed as acute appendicitis preoperatively with an ultrasound. However, intraoperatively, a product of conception-like material measuring 3 ∗ 3 cm was seen hanging from the right fimbrial end of the fallopian tube with a normal appendix. With an intraoperative diagnosis of spontaneous tubal abortion, histopathology of the resected mass showed chorionic villi lined by trophoblastic cells along with decidualized tissue, fibrinoid material, and blood clot. DISCUSSION: Ectopic pregnancy presenting as a right iliac fossa pain can mimic acute appendicitis. An abnormal ß-hCG pattern/level which doesn't correspond to the gestational age suggests the likely diagnosis of ectopic gestation. Transvaginal ultrasound is the preferred imaging modality for the evaluation of patients with suspected ectopic gestation. A urine pregnancy kit cannot always exclude an underlying ectopic pregnancy because of the associated false-negative results. CONCLUSION: Urgent laparotomy to prevent detrimental complications associated with ectopic gestation should be done. Surgeons should be aware of this suspicion as a false negative UPT can happen and misguide clinicians about the possible occurrence of ectopic pregnancy.