A metagenome-derived artificial intelligence modeling framework advances the predictive diagnosis and interpretation of petroleum-polluted groundwater.

Groundwater (GW) quality monitoring is vital for sustainable water resource management. The present study introduced a metagenome-derived machine learning (ML) model aimed at enhancing the predictive understanding and diagnostic interpretation of GW pollution associated with petroleum. In this framework, taxonomic and metabolic profiles derived from GW metagenomes were combined for use as the input dataset. By employing strategies that optimized data integration, model selection, and parameter tuning, we achieved a significant increase in diagnostic accuracy for petroleum-polluted GW. Explanatory artificial intelligence techniques identified petroleum degradation pathways and Rhodocyclaceae as strong predictors of a pollution diagnosis. Metagenomic analysis corroborated the presence of gene operons encoding aminobenzoate and xylene biodegradation within the de novo assembled genome of Rhodocyclaceae. Our genome-centric metagenomic analysis thus clarified the ecological interactions associated with microbiomes in breaking down petroleum contaminants, validating the ML-based diagnostic results. This metagenome-derived ML framework not only enhances the predictive diagnosis of petroleum pollution but also offers interpretable insights into the interaction between microbiomes and petroleum. The proposed ML framework demonstrates great promise for use as a science-based strategy for the on-site monitoring and remediation of GW pollution.

Recalcitrant Cutaneous Lupus Erythematosus during COVID-19 Pandemic: A Case Report.

A 30-year-old woman visited the dermatology and venereology clinic with red rashes on her cheeks with spreading wounds to the ears present for 6 months. Similar ailments were also found on the chest and upper arms accompanying black spots on both palms. Initially, red rashes appeared intermittently, observed around the eyes and cheeks, especially at sun exposure. Tenderness or pruritus was not present; however, the patient had joints ache, sore fingers, hair loss as well as frequent fever.

Machine learning reveals the complex ecological interplay of microbiome in a full-scale membrane bioreactor wastewater treatment plant.

Membrane bioreactor (MBR) systems are one of the most widely used wastewater treatment processes for various municipal and industrial waste streams. The present study aimed to advance the understanding of ecologically important keystone taxa that play an important role in full-scale MBR systems. A machine-learning (ML) modeling framework based on microbiome data was developed to successfully predict, with an average accuracy of >91.6%, the operational characteristics of three representative full-scale wastewater systems: an MBR, a conventional activated sludge system, and a sequencing batch reactor. ML-based feature-importance analysis identified Ferruginibacter as a keystone organism in the MBR system. The phylogeny and known ecophysiology of members of Ferruginibacter supported their role in metabolizing complex organic polymers (e.g., extracellular polymeric substances) in MBR systems characterized by high concentrations of mixed liquor suspended solids and a high solid retention time. ML regression modeling also revealed temporal patterns of Ferruginibacter in response to water temperature. ML modeling was thus successfully employed in the present study to investigate complex/non-linear relationships between keystone taxa and environmental conditions that cannot be detected using conventional approaches. Overall, our microbiome-data-enabled ML modeling approach represents a methodological advance for identifying keystone taxa and their complex ecological interactions, which has implications for the sustainable and predictive management of MBR systems.

A diagnostic challenge in an atypical variant of microcystic adnexal carcinoma mimicking ulcerative basal cell carcinoma: a case report and brief literature review.

Microcystic adnexal carcinoma (MAC) is a rare adnexal tumor with eccrine and pillar differentiations with a localized and aggressive nature, often misdiagnosed as other dermatoses. The most common clinical manifestations of MAC are yellowish or skin-colored papules, nodules, and plaques. However, in some rare cases an atypical manifestation such as ulceration that resembles malignancies such as basal cell carcinoma (BCC) can also occur. Diagnosis of MAC mainly relies on the aid of histopathology. Due to potential infiltration to other structures such as in perineural invasion, wide surgical excision or Mohs micrographic surgery is the preferred surgical option. We report the case of a 75-year-old male patient with ulcerative lesion on the forehead that clinically resembled BCC in addition to typical dermoscopic findings of BCC. However, histopathology confirmed a diagnosis of MAC, prompting physicians to be more aware of this condition when encountering chronic ulcerative lesions. After wide excision and a 1-year follow-up, the patient exhibited no signs of recurrences and will continue long-term follow-up.