Introduction: Whether bone metastasis is present at diagnosis determines curative versus systemic treatment of prostate cancer, yet referral for advanced staging relies on imperfect risk stratification; magnetic resonance imaging (MRI) radiomics has been proposed to predict metastatic status from routinely acquired staging imaging. Methods: This systematic review (PROSPERO CRD420261463568) searched PubMed, Embase and Cochrane CENTRAL for studies of radiomic, deep-learning or hybrid models predicting bone-metastatic status from prostate MRI; incremental value over clinical predictors, reporting completeness, radiomics methodology and risk of bias were appraised. Results: Six retrospective studies (1240 patients, 491 with bone metastasis, all from China; 322 contributed to the principal validation estimates) were included. Clinical-plus-radiomics models achieved validation areas under the receiver operating characteristic curve (AUCs) of 0.893–0.920 internally and 0.903 externally, with the highest estimate (0.928) from a multimodal model lacking clinical predictors. Only two studies compared clinical-only with combined models (AUC increments of 0.060 and 0.081). Conclusions: Reported discrimination was generally high, but current evidence cannot establish that radiomics adds value beyond routine clinical predictors. These models are best understood as predictive biomarkers rather than detection tools; a potential triage role among patients below routine staging thresholds remains untested, and transportability beyond the high-prevalence cohorts studied is unproven; prospective multicentre validation is required before clinical use.
Small extracellular vesicles (sEVs), commonly referred to as exosomes, are nanoscale vesicles that mediate intercellular communication and represent promising biotechnological platforms for disease diagnosis, therapeutic intervention, and drug delivery. sEVs derived from plants, mesenchymal stem cells, tumor cells, and immune cells display pronounced source-dependent heterogeneity shaped by parental cell lineage, differentiation status, and the local microenvironment. Such heterogeneity influences not only their biological properties, including physicochemical characteristics, molecular cargo profiles, and immunomodulatory functions, but also their manufacturing characteristics, in vivo tropism, and translational potential as biotherapeutics. Nevertheless, a comprehensive evaluation framework from a medical biotechnology perspective remains insufficiently established. In this review, the core biological features of multi-source sEVs are first outlined to establish a foundation for subsequent analysis. We then examine their biomedical applications and discuss biotechnological bottlenecks, including isolation and purification workflows, quality-control metrics, batch-to-batch variability, scalable biomanufacturing, and biosafety assessment. Particular attention is also given to the incompletely characterized regulatory networks of immune-cell-derived sEVs. Furthermore, we discuss how multi-omics profiling, standardized production protocols, and precision-engineering strategies may facilitate the functional optimization of source-specific sEVs. Collectively, this review provides an integrated medical biotechnology framework for evaluating multi-source sEVs and highlights practical considerations for accelerating the bench-to-bedside translation of sEV-based biotherapeutics.
A study of bacterial xylanases from Streptomyces prunicolor, Streptomyces carpinensis, and Streptomyces venezuelae was conducted. The xylanase-encoding genes were cloned from bacterial genomes. Recombinant producers based on E. coli M15(Rep4) and the pQE-30 vector were obtained, and the expression and purification of the recombinant proteins were carried out. Electrophoretically homogeneous preparations of four xylanases from the GH10 and GH11 families were obtained. The xylanases had a near-neutral pH optimum and moderate thermal stability (the enzymes were stable at 70 °C). The xylanases were more stable over a pH range of 5.0 to 8.0. The properties of the xylanases obtained correspond to the properties of most xylanase-producing bacteria of the Streptomyces genus described in the literature. Xylanases from the GH10 family of S. carpinensis form xylose, xylobiose and xylotetraose as main products of xylan hydrolysis. The GH10 xylanase from S. venezuelae hydrolyzes xylan into xylose, xylobiose, xylotriose, xylotetraose and xylopentaose. The main products of xylan hydrolysis by GH11 xylanase from S. prunicolor are xylobiose, xylopentaose and a trace amount of xylose. All four xylanases effectively saccharify cereals, indicating the biotechnological potential of these enzymes.
An artificial neural network (ANN)-based digital twin feed controller, aiming to optimise bioprocess performance, was implemented in fed-batch bioprocesses of Lactobacillus rhamnosus LRH30 for the production of biomass. The online data from several fed-batch bioprocesses in dairy-based cultivations with L. rhamnosus were utilised to develop an ANN that predicts the biomass and growth rate of L. rhamnosus in the process. This growth rate estimator was first validated through unit testing before being applied in model predictive control to select the best control action to drive feed addition to the process. The controller was implemented in fed-batch bioprocesses and the growth rate was estimated in real time, leading to the highest cell dry weight biomass concentrations obtained for L. rhamnosus LRH30 of 36.23 g/L in this study, with growth-rate overshoot and increased feed addition also contributing to the biomass accumulation despite imperfect setpoint tracking. The dose-dependent effect of L. rhamnosus LRH30 samples on cytokine secretion from murine macrophages was further explored, and an anti-inflammatory effect was found at a dosage of 10 mg/mL of bacterial cell dry weight. These experiments demonstrate the first steps in the implementation phase that will ultimately lead to a better controlled bioprocess with higher yields of L. rhamnosus biomass. Future research will focus on the continuous development of the ANN through the incorporation of more datasets, while simultaneously exploring the functional potential of L. rhamnosus LRH30.
Oat (Avena sativa L.) is generally considered a healthy and nutritious cereal and has gained further interest due to the generation of oat milk as an alternative nutritious beverage. However, during oat milling, about 20–30% hulls are produced. These are of low value, with small amounts used as feed or for combustion or are simply discarded. One of the downsides to consuming oat hulls is their abrasiveness because of high silica content but otherwise present a significant opportunity for biotransformation into value-added products derived from the milling industry in a context of upcycling. This review addresses some of the physico-chemical properties of oat hulls and discusses fermentative systems such as solid-state (SSF), semi-solid state (Semi-SSF) and submerged (SmF) fermentation to break down and texturize oat hulls into valuable products which can be used as food, feed or pet food. We also discuss the three fermentation systems (SSF, Semi-SSF and SmF) and their potential for oat hull valorization. With increasing interest in mycoproteins, we also discuss edible fungi as the ideal microorganisms capable not only of degrading oat hulls and increasing nutrient bioavailability but also of contributing microbial protein.
