Biotechnology and applied biochemistry最新文献

Carbapenem resistance among multidrug-resistant pathogens is a growing global concern, particularly in Asia. The increased use of colistin as a final treatment option for carbapenem-resistant Enterobacteriaceae has contributed to the development of colistin resistance. This study sought to identify the prevalence of carbapenemase and colistin resistance genes in carbapenem-resistant Klebsiella pneumoniae (CRKP) isolates from a Malaysian tertiary hospital. A total of 257 different CRKP isolates were obtained from various clinical specimens between January 2017 and November 2020. Antibiotic susceptibility testing and multiplex polymerase chain reaction were performed to detect carbapenemase genes (bla_NDM, bla_KPC, and bla_OXA) and the colistin resistance gene mcr-1. The bla_NDM gene was found in 247 isolates (95.3%), bla_OXA in one (0.4%), and mcr-1 in two (0.8%). Two isolates co-harbored bla_NDM and mcr-1, whereas one had both bla_NDM and bla_OXA. No bla_KPC gene was detected. The highest number of bla_NDM-positive isolates came from endotracheal tubes specimens (21.8%). Males accounted for 65.7% of cases, with the highest prevalence in patients aged 61-80. Ertapenem showed the highest resistance (99.6%) among the carbapenems tested. This study highlights a high prevalence of bla_NDM in CRKP and reports the first detection of mcr-1 in this hospital. These findings underscore the urgent need for the judicious use of colistin in managing infections caused by CRKP.

Microbes are known to produce various bioactive compounds that find applications in the biomedical field. Out of many bioactive compounds, pigments synthesized from microbes have gained industrial attention. Pigments have been produced from various bacteria, fungi, and algae. Recently, microbial pigment-mediated nanoparticle (NP) synthesis is gaining importance due to its application in various fields. Various pigments such as melanin, phycocyanin, monascus, xanthomonadin, prodigiosin, carotenoid, flexirubin, canthaxanthin, and fucoxanthin have been utilized to generate different types of NPs with diverse shapes and sizes. Generally, silver and gold NPs have mostly been made using microbial pigments. The benefit of utilizing NPs produced through these pigments is that this method is eco-friendly and cost-effective. In addition to this, these NPs can be used in diverse biomedical sectors. These NPs act as anticancer, antibacterial, antifungal, and antioxidant agents. The present review sheds light on various microbial pigments that have been utilized for the synthesis of various NPs and their application in medicine and diagnostics and their future prospects.

Targeted protein degradation has emerged as a revolutionary approach to the selective elimination of disease-causing proteins that provides new strategies in the treatment of a variety of conditions. Currently, two major techniques in this area are known as proteolysis targeting chimeras (PROTACs) and molecular glue degraders (MGDs). MGDs can be natural or of synthetic origin, and only limited studies were performed with marine-derived MGDs. The study is a representation of the systematic review of literature in databases like Scopus, Web of Science, and PubMed about the MGDs from marine sources and deals with an outline of their therapeutic applications. MGDs derived from the ocean have shown great promise in infectious diseases, autoimmune disorders, cancer, and neurotoxicity-related disorders. Among them are Manumycin A against breast cancer and Khalalide F for treating neurotoxicity. Their identification and characterization are enormous challenges that, notwithstanding the therapeutic promise of MGDs, require advanced technologies and very specialized skills. Their development process is also complex legally and ethically given the imperative to access and use marine genetic resources within the parameters of the Nagoya Protocol on Access and Benefit Sharing. By setting a limit to the exploitation of genetic materials, in this case, marine genetic resources, further research developments in this particular area have especially become difficult due to the Nagoya Protocol. Marine-derived MGDs are opening a new frontier in drug discovery for the treatment of multi-factorial diseases with minimal/no side effects. Having the potential to revolutionize future treatment strategies, overcoming technical, legal, and ethical challenges will be important to realizing their full potential and assuring equitable access to such ground-breaking resources.

Sinus pericranii (SP) is a rare venous anomaly characterized by abnormal communication between intracranial dural sinuses and extracranial veins. Beyond its structural features, SP may function as an auxiliary venous outflow pathway and a buffer for transient intracranial pressure changes, albeit with limited capacity compared to major dural sinuses. To synthesize current knowledge on the embryology and pathophysiological mechanisms of SP, and to discuss diagnostic and management strategies. This narrative review integrates evidence from published literature on SP's anatomical features, hemodynamic role, associated conditions, and imaging characteristics, with emphasis on its physiological and pathological significance. SP development is associated with factors such as elevated intracranial pressure, congenital cranial anomalies, trauma, and vascular malformations. Pathophysiological changes can predispose to cerebral venous sinus thrombosis and intracranial hypertension. Advances in magnetic resonance (MR) venography and 3D-CT have improved diagnostic accuracy and anatomical mapping, supporting decisions between conservative observation and surgical or endovascular intervention. Understanding both the physiological and pathological roles of SP enhances diagnostic precision and guides multidisciplinary treatment planning. Integrating anatomical and pathophysiological perspectives offers a comprehensive framework for clinical decision-making.

Recent studies have highlighted the important role of PANoptosis in asthma. The purpose of this work was to find long noncoding RNAs (lncRNAs) associated with PANoptosis and assess their potential as asthma diagnostic biomarkers. We examined two publicly accessible datasets (GSE165934 and GSE195599). Diagnostic lncRNAs linked to PANoptosis were screened using logistic regression and least absolute shrinkage and selection operator (LASSO) models. Subcellular localization and immune microenvironmental features were assessed. A competitive endogenous RNA (ceRNA) network was created by combining starBase predictions with differentially expressed mRNAs. The levels of potential lncRNAs' transcriptional expression were further confirmed. Four of the seven differentially expressed lncRNAs found in both datasets (LUCAT1, RC3H1-IT1, FAM157A, and TTN-AS1) were verified as diagnostic lncRNAs linked to PANoptosis. When compared to controls, asthma samples showed clear changes in the makeup of immune cells and signaling pathways. Three lncRNAs were shown to be mostly localized in the cytoplasm, according to subcellular localization studies. A ceRNA regulation network with 154 interaction partners and 72 nodes was created. In comparison to normal samples, asthma samples exhibited substantially reduced expression of all four diagnostic lncRNAs. This study provides the first evidence linking PANoptosis-related lncRNAs to asthma. The identified lncRNAs (LUCAT1, RC3H1-IT1, FAM157A, and TTN-AS1) may serve as clinically relevant diagnostic biomarkers and offer novel insights into asthma pathogenesis, potentially supporting the development of improved therapeutic strategies.

This study investigates the efficacy of advanced deep learning techniques, specifically convolutional neural network (CNN) (U-Net) and single-shot multibox detector (SSD), in enhancing the early detection of brain tumors, thereby facilitating timely medical intervention. Accurate brain tumor detection is paramount in medical image analysis as it involves the precise identification and localization of abnormal growths within the brain. Conventional diagnostic approaches often rely on manual analysis conducted by radiologists, which are susceptible to human error and influenced by variability in tumor size, shape, and location. In our research, we leverage U-Net, a CNN widely recognized for its effectiveness in medical image segmentation, alongside SSD, an established object detection algorithm. The results indicate that the U-Net model achieved an impressive accuracy of 97.73%, demonstrating a high level of effectiveness in segmenting brain tumors with exceptional precision. Conversely, the SSD model secured an accuracy of 58%, which, while comparatively lower, suggests that it may still serve as a valuable supplementary tool in specific scenarios and for broader applications in identifying tumor regions within medical scans. Our findings illuminate the potential of utilizing U-Net for high-precision brain tumor detection, reinforcing its position as a leading method in medical imaging. Overall, the study reinforces the important role of deep learning methods in improving early detection outcomes in neuro-oncology and highlights avenues for further exploration in enhancing diagnostic accuracy.

Porcine pancreas lipase (PPL) has been used as an efficient green biocatalyst for the multicomponent synthesis of pyrido[2,3-d]pyrimidine derivatives and expanding the biocatalytic promiscuity of lipase in organic synthesis. This robust procedure involves the condensation of various aromatic aldehydes, malononitrile, and 6-amino-1,3-dimethyl uracil in a 1:1:1 molar ratio at 50°C, catalyzed by PPL in an equimolar mixture of water and ethanol (1:1, v/v). The influence of the reaction conditions, including enzyme origin, organic solvents, temperature, and the amount of biocatalyst on the reaction course, was examined. Notably, PPL displayed remarkable catalytic activity, enabling the synthesis of diverse pyrido[2,3-d]pyrimidine derivatives with good to excellent yields (77%-94%). A promiscuous lipase-catalyzed carbon-nitrogen bond formation is presented, accommodating a variety of aromatic aldehydes. This method exhibits several key advantages, including the use of environment-friendly solvents, a nontoxic biocatalyst, mild reaction conditions, straightforward workup procedures, and excellent product yields.

The global pursuit of sustainable and climate-resilient technologies has accelerated interest in waste valorization for biosurfactant production. Biosurfactants, recognized as eco-friendly alternatives to synthetic surfactants, offer versatile applications across industries due to their biodegradability, low toxicity, and surface-active properties. Despite having number of applications and advantages, commercialization remains difficult primarily due to the high cost, with substrates alone contributing over 50% of the total production cost. Leveraging waste as a feedstock for production has opened new avenues by reducing production costs by 10%-30% while simultaneously promoting environmental sustainability. Waste-derived biosurfactants not only offer significant cost reductions and yield (1 g/L-60 g/L depending on feedstock and strain) and environmental advantages over conventional substrates but also advance circular bioeconomy principles by transforming waste into valuable products and aiding in waste management. This review synthesizes current advancements in biosurfactant classification, microbial sources, and fermentation strategies, with a particular emphasis on the valorization of low-cost waste feedstocks, such as food processing byproducts, lignocellulosic agricultural residues, and industrial effluents. The review further elaborates emerging biotechnological approaches aimed at improving yield, functionality, and process scalability. Major emphasis is on the utilization of waste to address both environmental challenges and economic feasibility in biosurfactant synthesis over conventional substrate-derived biosurfactant.

The objective of this investigation was to develop a diagnostic model for osteoarthritis (OA) by integrating immune cell profiling with transcriptomic signatures. Four gene expression datasets related to OA were downloaded from the GEO database. CIBERSORT was employed to evaluate the proportion of different immune cell types. Hub immune cells were selected using three distinct optimization algorithms (LASSO, RFE, and RF). Differentially expressed genes (DEGs) between OA and control samples were screened using the limma package. Subsequently, function analysis and protein-protein interaction (PPI) analysis were conducted. Topology analysis based on four algorithms was performed, and hub genes were identified by overlapping the results of these four algorithms. A diagnostic model was constructed and validated using the ROC curve method. Pearson correlation coefficients between hub immune cell populations and candidate genes were computed using the cor() function in R. Seven types of differentially abundant immune cells were identified between the two groups. After analysis with the RF, LASSO, and RFE algorithms, five overlapping immune cells, namely, T cell CD4 memory resting, NK cell activated, T cell CD4 naive, mast cell resting, and mast cell activated, were selected as hub immune cells. A total of 578 DEGs were selected, which were implicated in the MAPK signaling pathway, focal adhesion, and osteoclast differentiation. Following PPI analysis, five hub genes (CXCL8, EEF1A1, IL1B, EEF2, and IL6) were obtained. The diagnostic model demonstrated excellent performance. Significant correlations were observed between the hub genes and immune cell populations. Through systematic analysis, we identified five key immune cell types and five hub genes associated with immune infiltration in OA. These biomarkers were subsequently utilized to construct a diagnostic prediction model.

Lonicera japonica Flos (LJF) possesses antiviral, antioxidant, and anti-inflammatory properties, but its mechanisms against rheumatoid arthritis (RA) are not well understood. This study aims to clarify LJF's molecular mechanisms in treating RA. Active components and targets of LJF and RA were determined using databases such as TCMSP, GeneCards, and OMIM. Protein-protein interaction (PPI) network analysis and pathway enrichment analysis (via KEGG as well as GO databases) were conducted to predict potential targets and pathways. Molecular docking identified potential targets of three specific LJF components. The anti-arthritic effects of LJF were evaluated in vivo using a collagen-induced arthritis (CIA) rat model. Seventeen active LJF components and 126 potential anti-RA targets were identified. Key compounds include quercetin, luteolin, kaempferol, beta-carotene, and beta-sitosterol. Main targets are AKT1, TP53, JUN, TNF, IL6, CASP3, EGFR, RELA, IL1B, and VEGFA. KEGG analysis suggested the involvement of the PI3K-AKT, TNF, and IL-17 pathways. Molecular docking demonstrated that β-sitosterol, quercetin, and luteolin effectively bind to AKT1, IL6, JUN, TNF, and TP53. In vivo studies confirmed that LJF reduces pathological damage and inflammatory markers (TNF-α and IL-6) in a dose-dependent manner, supporting its anti-RA effects via AKT and RELA through PI3K-AKT and NF-κB pathways. This research identified the PI3K-AKT and NF-κB signaling pathways as key targets of LJF, highlighting their roles in its anti-RA effects. These findings point to the possibility of advancing research and clinical use of LJF in RA treatment.