Biotechnology and applied biochemistry最新文献

This research seeks to identify and verify the prognostic signature of immune-related RNA of papillary thyroid carcinoma (PTC). The Cancer Genome Atlas (TCGA) database provided the PTC samples and clinical data. Using the limma program, differentially expressed mRNAs and lncRNAs (DElncRNAs) were found. A risk score (RS) model was created by using univariate and multivariate Cox regression analyses to find lncRNAs associated with overall survival prognosis. The association between the lncRNA prognostic signature and the invasion of several immune cell subtypes was investigated using the Tumor Immune Estimation Resource (TIMER). To verify the expression of the prognostic signature, RT-PCR was utilized. From TCGA, 491 PTC tumor samples were acquired, comprising 18497 mRNAs and 2525 lncRNAs. There were 138 DElncRNAs and 494 differentially expressed mRNAs. Six DElncRNAs with independent prognoses were then selected from a total of 138 DElncRNAs. According to the TIMER database, the 6-DElncRNA PTC prognostic signature was correlated with the invasion of several immune cell subtypes. The expression patterns of four DElncRNAs matched the database results, according to RT-PCR analysis. Six lncRNAs were found to constitute a predictive characteristic for PTC in this study, and these signatures were connected to the infiltration of different immune cell subtypes. These findings provide a potential basis for the development of diagnostic biomarkers and immunotherapeutic stratification in PTC.

Hydrogen sulfide (H₂S) has emerged as a crucial signaling molecule with profound physiological and pathological roles, sparking interest in its biotechnological production through microbial engineering. The potential applications of microbially produced H₂S in medicine, agriculture, and industry have driven significant research advancements. This review comprehensively examines the latest developments in engineering microbes for H₂S production. Key topics include genetic and metabolic engineering strategies that enhance H₂S biosynthesis, innovative production methods, and practical applications of microbial H₂S. Additionally, we address the technical and biological challenges faced in optimizing H₂S production, such as maintaining microbial viability and ensuring controlled release. The review also explores future directions in the field, emphasizing the need for sustainable and efficient production systems, the potential for scalable industrial applications, and the integration of H₂S-producing microbes in therapeutic and agricultural settings. Overall, this review provides a detailed overview of the current state and future prospects of H₂S production, highlighting its significance in various biotechnological applications.

The glory lily (Gloriosa superba L.) is a medicinally important and endangered plant found in the Western Ghats of Maharashtra, India. It harbors a diverse community of endophytic fungi with immense biotechnological potential. To explore the potential medicinal properties of the endophytes, this study sought to isolate, identify, and assess the bioactivity of endophytic fungi from various plant tissues of G. superba, such as roots, stems, leaves, and flowers. Twenty-one endophytic fungal cultures were isolated and identified with molecular and morphological analyses. Bioactivity assays demonstrated their antimicrobial, antioxidant, and enzyme-inhibitory properties, supported by the production of secondary metabolites, such as alkaloids, phenolics, and others. These findings highlight the potential of G. superba-associated fungal endophytes as a sustainable source of pharmacologically active compounds, reducing the reliance on harvesting the endangered host plant from its wild habitats. While exploring its endophytes for various applications, conservation strategies, including tissue culture propagation and habitat preservation, should be employed to protect wild G. superba populations. This integrated approach would support biodiversity conservation and sustainable utilization of fungal endophytes isolated from this endangered plant species.

The enzymatic hydrolysis of soybean proteins was investigated using four enzymes (alcalase, flavourzyme, neutrase, and papain) in combination with different concentrations of natural deep eutectic solvents (NADESs) and ionic liquids (ILs). The goal was to assess the influence of these alternative green solvents on enzymatic efficiency. First, the hydrolysis kinetics were studied over a period of 240 min, in which the results showed an optimum time of 150 min, allowing high rates of soluble peptides without system saturation. The protein hydrolysis rate (PHR) was used as an indicator of enzymatic efficiency to evaluate the effect of NADESs based on cholinium chloride (ChCl) with hydrogen bond donors (urea, glycerol, lactic acid [Lac], and acetic acid [Ac]) and ILs formed with cholinium-cation and lactate or acetate anions. The results showed that the alcalase presented the highest hydrolysis values in the presence of ChCl:urea, ChCl:glycerol, and [Ch][Lac], in which a strong hydrolytic activity was observed at concentrations of 50% of the solvent. In contrast, solvents with acidic HBDs (ChCl:lactic acid and ChCl:acetic acid) and [Ch][Ac] showed strong inhibitory effects on enzymatic activity. Molecular docking revealed that while acetate directly interacted with Ser221 of alcalase, the nucleophilic residue in the catalytic triad, lactate formed more distributed and less disruptive interactions. Urea showed strong affinity with peripheral residues, preserving enzymatic structure and functionality. These results demonstrate that combining experimental data and molecular docking analysis constitutes a strategic approach for the rational design of green solvents, optimizing their application as cosolvents in biocatalytic reactions.

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.