Objective: To isolate, purify, characterize, elucidate structure and evaluate bioactive compounds from the sponge-derived Salinispora sp. FS-0034.
Methods: The symbiotic actinomycete strain FS-0034 with an interesting bioactivity profile was isolated from the Fijian marine sponge Theonella sp. Based on colony morphology and obligatory requirement of seawater for growth, and mycelia morphological characteristics the isolate FS-0034 was identified as a Salinispora sp. The bioactive compound was identified by using various spectral analysis of ultraviolet, high resolution electrospray ionization mass spectroscopy, 1H nuclear magnetic resonance, correlated spectroscopy and heteronuclear multiple bond coherence spectral data. A minimum inhibitory concentration assay were performed to evaluate the biological properties of the pure compound against multi-drug resistant pathogens.
Results: Bioassay guided fractionation of the ethyl acetate extract of the culture of Salinispora sp. FS-0034 by different chromatographic methods yielded the isolation of an antibacterial compound, which was identified as rifamycin W (compound 1). Rifamycin W was reported for its potent antibacterial activity against methicillin-resistant Staphylococcus aureus, wild typeStaphylococcus aureus and vancomycin-resistant Enterococcus faecium and displayed minimum inhibitory concentrations of 15.62, 7.80 and 250.00 μg/mL, respectively.
Conclusions:The present study reported the rifamycin W from sponge-associated Salinispora sp. and it exhibited appreciable antibacterial activity against multi-drug resistant human pathogens which indicated that sponge-associated Actinobacteria are significant sources of bioactive metabolites.

Bladder cancer remains a significant health challenge. Early and accurate diagnoses are crucial for effective treatment and improved patient outcomes. In recent years, artificial intelligence (AI) has emerged as a powerful tool in the medical field, showing great promise in advancing the bladder cancer diagnosis. This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. AI systems can analyze vast amounts of data from various sources, such as medical imaging, genomic data, and electronic health records, enabling the identification of subtle patterns and biomarkers that may indicate the presence of bladder cancer. These systems have demonstrated high accuracy in detecting cancerous lesions in imaging modalities such as cystoscopy, ultrasonography, and computed tomography scans, often surpassing human performance. Moreover, AI-driven diagnostic tools can assist in risk stratification, predicting disease progression, and personalizing treatment plans, thereby contributing to more targeted and effective therapies.

Bladder cancer remains a significant health challenge. Early and accurate diagnoses are crucial for effective treatment and improved patient outcomes. In recent years, artificial intelligence (AI) has emerged as a powerful tool in the medical field, showing great promise in advancing the bladder cancer diagnosis. This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. AI systems can analyze vast amounts of data from various sources, such as medical imaging, genomic data, and electronic health records, enabling the identification of subtle patterns and biomarkers that may indicate the presence of bladder cancer. These systems have demonstrated high accuracy in detecting cancerous lesions in imaging modalities such as cystoscopy, ultrasonography, and computed tomography scans, often surpassing human performance. Moreover, AI-driven diagnostic tools can assist in risk stratification, predicting disease progression, and personalizing treatment plans, thereby contributing to more targeted and effective therapies.

Bladder cancer remains a significant health challenge. Early and accurate diagnoses are crucial for effective treatment and improved patient outcomes. In recent years, artificial intelligence (AI) has emerged as a powerful tool in the medical field, showing great promise in advancing the bladder cancer diagnosis. This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. AI systems can analyze vast amounts of data from various sources, such as medical imaging, genomic data, and electronic health records, enabling the identification of subtle patterns and biomarkers that may indicate the presence of bladder cancer. These systems have demonstrated high accuracy in detecting cancerous lesions in imaging modalities such as cystoscopy, ultrasonography, and computed tomography scans, often surpassing human performance. Moreover, AI-driven diagnostic tools can assist in risk stratification, predicting disease progression, and personalizing treatment plans, thereby contributing to more targeted and effective therapies.

INTRODUCTIONRenal transplant rejection involves both immunological and non-immunological factors. The objective of the present study was to investigate the association between immunological factors, such as serum interleukin 6 (IL-6) and tumour necrosis factor alpha (TNF-α), and non-immunological parameters, such as age, serum creatinine (SCr), creatinine clearance (CrCl) and dyslipidaemia, in renal transplant recipients (RTRs).

METHODSThis study included 90 RTRs and 90 healthy controls. Biochemical parameters, including serum IL-6 and TNF-α, were estimated using standard protocols. CrCl was calculated using the Cockroft-Gault equation, and the type of rejection was confirmed on biopsy. Student's t-test and univariate and multivariate analyses were performed using the Statistical Package for the Social Sciences for Windows version 15.

RESULTSThe mean levels of serum IL-6 and TNF-αwere significantly higher in RTRs than in the control group (p < 0.001). These parameters were also found to be significantly different between the transplant rejection (TR) and transplant stable (TS) groups (p < 0.001). CrCl was significantly decreased in the TR group when compared to the TS group (p < 0.001). The two cytokines, IL-6 and TNF-α, correlated significantly with all metabolic parameters, such as SCr, CrCl and dyslipidaemia. Multiple regression analysis showed that TNF-α and CrCl were the strongest predictors of IL-6.

CONCLUSIONWe conclude that immunological factors, as well as non-immunological factors such as CrCl, SCr and dyslipidaemia, play important roles in the pathogenesis of graft rejection and renal graft dysfunction.

Adult ; Biomarkers ; blood ; Biopsy ; Creatinine ; blood ; Female ; Follow-Up Studies ; Graft Rejection ; blood ; pathology ; Humans ; Interleukin-6 ; blood ; Kidney ; pathology ; Kidney Transplantation ; Male ; Predictive Value of Tests ; Retrospective Studies ; Time Factors ; Tumor Necrosis Factor-alpha ; blood