In-line porosity and hardness monitoring of tablets by means of optical coherence tomography.

In-line monitoring of critical quality attributes (CQAs) during a tableting process is an essential step toward a real-time release strategy. Such CQAs can be the tablet mass, the API content, dissolution, hardness and tensile strength. Since dissolution testing is laborious and time-consuming and cannot be performed in-line, it is desirable to replace dissolution testing with predictive models based on other CQAs that affect the dissolution characteristics, such as the tablet porosity and hardness. Traditionally, porosity is determined offline via gas adsorption methods or other techniques, such as Terahertz spectroscopy or gas in scattering media absorption spectroscopy. Tablet hardness is typically established using a hardness tester. While these destructive tests can readily be performed at-line, they have limited applicability in in-line settings for a high-percentage inspection. Optical coherence tomography (OCT) has recently been proposed as a possible tool for determining quality attributes. This work describes the first application of OCT for the prediction of tablet porosity and hardness. OCT measurements of tablets produced in a ConsiGma 25™ tableting line and a Stylcam 200R compaction simulator in several compaction force settings were made and correlated with the porosity and hardness. It was demonstrated that OCT can easily be installed in-line and provide real-time information about critical material attributes. These insights confirm the applicability of OCT as a real-time quality control tool and its potential to replace time-consuming and destructive offline measurements.

In-line NIR coupled with machine learning to predict mechanical properties and dissolution profile of PLA-Aspirin.

In the production of polymeric drug delivery devices, dissolution profile and mechanical properties of the drug loaded polymeric matrix are considered important Critical Quality Attributes (CQA) for quality assurance. However, currently the industry relies on offline testing methods which are destructive, slow, labour intensive, and costly. In this work, a real-time method for predicting these CQAs in a Hot Melt Extrusion (HME) process is explored using in-line NIR and temperature sensors together with Machine Learning (ML) algorithms. The mechanical and drug dissolution properties were found to vary significantly with changes in processing conditions, highlighting that real-time methods to accurately predict product properties are highly desirable for process monitoring and optimisation. Nonlinear ML methods including Random Forest (RF), K-Nearest Neighbours (KNN) and Recursive Feature Elimination with RF (RFE-RF) outperformed commonly used linear machine learning methods. For the prediction of tensile strength RFE-RF and KNN achieved R 2 values 98% and 99%, respectively. For the prediction of drug dissolution, two time points were considered with drug release at t = 6 h as a measure of the extent of burst release, and t = 96 h as a measure of sustained release. KNN and RFE-RF achieved R 2 values of 97% and 96%, respectively in predicting the drug release at t = 96 h. This work for the first time reports the prediction of drug dissolution and mechanical properties of drug loaded polymer product from in-line data collected during the HME process. Supplementary Information: The online version contains supplementary material available at 10.1186/s42252-024-00063-5.

Electrochemical biosensors on microfluidic chips as promising tools to study microbial biofilms: a review.

Microbial biofilms play a pivotal role in microbial infections and antibiotic resistance due to their unique properties, driving the urgent need for advanced methodologies to study their behavior comprehensively across varied environmental contexts. While electrochemical biosensors have demonstrated success in understanding the dynamics of biofilms, scientists are now synergistically merging these biosensors with microfluidic technology. This combined approach offers heightened precision, sensitivity, and real-time monitoring capabilities, promising a more comprehensive understanding of biofilm behavior and its implications. Our review delves into recent advancements in electrochemical biosensors on microfluidic chips, specifically tailored for investigating biofilm dynamics, virulence, and properties. Through a critical examination of these advantages, properties and applications of these devices, the review highlights the transformative potential of this technology in advancing our understanding of microbial biofilms in different settings.

In Situ Studies on the Influence of Surface Symmetry on the Growth of MoSe2 Monolayer on Sapphire Using Reflectance Anisotropy Spectroscopy and Differential Reflectance Spectroscopy.

The surface symmetry of the substrate plays an important role in the epitaxial high-quality growth of 2D materials; however, in-depth and in situ studies on these materials during growth are still limited due to the lack of effective in situ monitoring approaches. In this work, taking the growth of MoSe2 as an example, the distinct growth processes on Al2O3 (112¯0) and Al2O3 (0001) are revealed by parallel monitoring using in situ reflectance anisotropy spectroscopy (RAS) and differential reflectance spectroscopy (DRS), respectively, highlighting the dominant role of the surface symmetry. In our previous study, we found that the RAS signal of MoSe2 grown on Al2O3 (112¯0) initially increased and decreased ultimately to the magnitude of bare Al2O3 (112¯0) when the first layer of MoSe2 was fully merged, which is herein verified by the complementary DRS measurement that is directly related to the film coverage. Consequently, the changing rate of reflectance anisotropy (RA) intensity at 2.5 eV is well matched with the dynamic changes in differential reflectance (DR) intensity. Moreover, the surface-dominated uniform orientation of MoSe2 islands at various stages determined by RAS was further investigated by low-energy electron diffraction (LEED) and atomic force microscopy (AFM). By contrast, the RAS signal of MoSe2 grown on Al2O3 (0001) remains at zero during the whole growth, implying that the discontinuous MoSe2 islands have no preferential orientations. This work demonstrates that the combination of in situ RAS and DRS can provide valuable insights into the growth of unidirectional aligned islands and help optimize the fabrication process for single-crystal transition metal dichalcogenide (TMDC) monolayers.

Machine learning in microalgae biotechnology for sustainable biofuel production: Advancements, applications, and prospects.

This review explores the critical role of machine learning (ML) in enhancing microalgae bioprocesses for sustainable biofuel production. It addresses both technical and economic challenges in commercializing microalgal biofuels and examines how ML can optimize various stages, including identification, classification, cultivation, harvesting, drying, and conversion to biofuels. This review also highlights the integration of ML with technologies such as the Internet of Things (IoT) for real-time monitoring and management of bioprocesses. It discusses the adaptability and flexibility of ML in the context of microalgae biotechnology, focusing on diverse algorithms such as Artificial Neural Networks, Support Vector Machines, Decision Trees, and Random Forests, while emphasizing the importance of data collection and preparation. Additionally, current ML applications in microalgae biofuel production are reviewed, including strain selection, growth optimization, system monitoring, and lipid extraction.

Application of shadowgraph imaging (SGI) particle characterisation data to interpret the impact of varying test conditions on powder dissolution and to develop an automated agglomeration identification method (AIM) in the USP flow-through apparatus.

The aims of this work were 1) to explore the application of shadowgraph imaging (SGI) as a real time monitoring tool to characterize ibuprofen particle behaviour during dissolution testing under various conditions in the USP 4 flow-through apparatus and 2) to investigate the potential to develop an SGI-based automated agglomeration identification method (AIM) for real time agglomerate detection during dissolution testing. The effect of surfactant addition, changes in the drug mass and flow rate, the use of sieved and un-sieved powder fractions, and the use of different drug crystal habits were investigated. Videos at every sampling time point during dissolution were taken and analysed by SGI. The AIM was developed to characterize agglomerates based on two criteria - size and solidity. All detections were confirmed by manual video observation and a reference agglomerate data set. The method was validated under new dissolution conditions with un-sieved particles. Characterisation of particle dispersion behaviour by SGI enabled interpretation of the impact of dissolution test conditions. Higher numbers of early detections reflected greater dissolution rates with increased surfactant concentration, using sieved fraction or plate-shaped crystals, but was impacted by drug mass tested. An AIM was successfully developed and applied to detect agglomerates during dissolution, suggesting potential, with appropriate method development, for application in quality control.

Dual-Purpose Aptamer-Based Sensors for Real-Time, Multiplexable Monitoring of Metabolites in Cell Culture Media.

The availability of high-frequency, real-time measurements of the concentrations of specific metabolites in cell culture systems will enable a deeper understanding of cellular metabolism and facilitate the application of good laboratory practice standards in cell culture protocols. However, currently available approaches to this end either are constrained to single-time-point and single-parameter measurements or are limited in the range of detectable analytes. Electrochemical aptamer-based (EAB) biosensors have demonstrated utility in real-time monitoring of analytes in vivo in blood and tissues. Here, we characterize a pH-sensing capability of EAB sensors that is independent of the specific target analyte of the aptamer sequence. We applied this dual-purpose EAB to the continuous measurement of pH and phenylalanine in several in vitro cell culture settings. The miniature EAB sensor that we developed exhibits rapid response times, good stability, high repeatability, and biologically relevant sensitivity. We also developed and characterized a leak-free reference electrode that mitigates the potential cytotoxic effects of silver ions released from conventional reference electrodes. Using the resulting dual-purpose sensor, we performed hourly measurements of pH and phenylalanine concentrations in the medium superfusing cultured epithelial tumor cell lines (A549, MDA-MB-23) and a human fibroblast cell line (MRC-5) for periods of up to 72 h. Our scalable technology may be multiplexed for high-throughput monitoring of pH and multiple analytes in support of the broad metabolic qualification of microphysiological systems.

Real-Time, Non-Invasive Monitoring of Neuronal Differentiation Using Intein-Enabled Fluorescence Signal Translocation in Genetically Encoded Stem Cell-Based Biosensors.

Real-time and non-invasive monitoring of neuronal differentiation will help increase our understanding of neuronal development and help develop regenerative stem cell therapies for neurodegenerative diseases. Traditionally, reverse transcription-polymerase chain reaction (RT-PCR), western blotting, and immunofluorescence (IF) staining have been widely used to investigate stem cell differentiation; however, their limitations include endpoint analysis, invasive nature of monitoring, and lack of single-cell-level resolution. Several limitations hamper current approaches to studying neural stem cell (NSC) differentiation. In particular, fixation and staining procedures can introduce artificial changes in cellular morphology, hindering our ability to accurately monitor the progression of the process and fully understand its functional aspects, particularly those related to cellular connectivity and neural network formation. Herein, we report a novel approach to monitor neuronal differentiation of NSCs non-invasively in real-time using cell-based biosensors (CBBs). Our research efforts focused on utilizing intein-mediated protein engineering to design and construct a highly sensitive biosensor capable of detecting a biomarker of neuronal differentiation, hippocalcin. Hippocalcin is a critical protein involved in neurogenesis, and the CBB functions by translocating a fluorescence signal to report the presence of hippocalcin externally. To construct the hippocalcin sensor proteins, hippocalcin bioreceptors, AP2 and glutamate ionotropic receptor AMPA-type subunit 2 (GRIA2), were fused to each split-intein carrying split-nuclear localization signal (NLS) peptides, respectively, and a fluorescent protein was introduced as a reporter. Protein splicing (PS) was triggered in the presence of hippocalcin to generate functional signal peptides, which promptly translocated the fluorescence signal to the nucleus. The stem cell-based biosensor showed fluorescence signal translocation only upon neuronal differentiation. Undifferentiated stem cells or cells that had differentiated into astrocytes or oligodendrocytes did not show fluorescence signal translocation. The number of differentiated neurons was consistent with that measured by conventional IF staining. Furthermore, this approach allowed for the monitoring of neuronal differentiation at an earlier stage than that detected using conventional approaches, and the translocation of fluorescence signal was monitored before the noticeable expression of class III ß-tubulin (TuJ1), an early neuronal differentiation marker. We believe that these novel CBBs offer an alternative to current techniques by capturing the dynamics of differentiation progress at the single-cell level and by providing a tool to evaluate how NSCs efficiently differentiate into specific cell types, particularly neurons.

Fluorescence lifetime imaging of human pancreatic lipase activity using a novel probe for early diagnosis of severe acute pancreatitis.

Severe Acute Pancreatitis, a serious condition caused by factors such as gallstones and chronic excessive alcohol consumption, with a very high mortality rate. Human pancreatic lipase (hPL) is a key digestive enzyme and abnormal activity levels of this enzyme are important indicators for diagnosing and monitoring pancreatic diseases. A fluorescent probe, LPP, has been developed to monitor the activity of hPL, especially in cases of SAP. The probe is based on cyanine isoindole derivatives, in vitro experiments confirmed the high specificity and sensitivity of the probe, with a detection limit of 0.012 U/mL, reactions completed within 10 min, and effective monitoring of pancreatic lipase activity in various biological samples. The stability and low cytotoxicity of LPP make it suitable for clinical applications, providing new tools and perspectives for the research and treatment of pancreatic diseases and related metabolic abnormalities. In addition, the change in fluorescence lifetime after the reaction of the probe with lipase allows for fluorescence lifetime imaging (FLIM), effectively monitoring the dynamic changes of hPL and enabling early diagnosis and monitoring of pancreatitis. This research not only enhances the understanding of pancreatic lipase activity detection but also has the potential to improve the diagnostics and treatment of pancreatitis.

Systematic Review of IoT-Based Solutions for User Tracking: Towards Smarter Lifestyle, Wellness and Health Management.

The Internet of Things (IoT) base has grown to over 20 billion devices currently operational worldwide. As they greatly extend the applicability and use of biosensors, IoT developments are transformative. Recent studies show that IoT, coupled with advanced communication frameworks, such as machine-to-machine (M2M) interactions, can lead to (1) improved efficiency in data exchange, (2) accurate and timely health monitoring, and (3) enhanced user engagement and compliance through advancements in human-computer interaction. This systematic review of the 19 most relevant studies examines the potential of IoT in health and lifestyle management by conducting detailed analyses and quality assessments of each study. Findings indicate that IoT-based systems effectively monitor various health parameters using biosensors, facilitate real-time feedback, and support personalized health recommendations. Key limitations include small sample sizes, insufficient security measures, practical issues with wearable sensors, and reliance on internet connectivity in areas with poor network infrastructure. The reviewed studies demonstrated innovative applications of IoT, focusing on M2M interactions, edge devices, multimodality health monitoring, intelligent decision-making, and automated health management systems. These insights offer valuable recommendations for optimizing IoT technologies in health and wellness management.

Multicomponent Gas Sensing Fiber Probe System Based on Platinum Coated Capillary Enhanced Raman Spectroscopy.

This paper proposes a novel multicomponent gas-sensing optical fiber probe system. It utilizes a precisely engineered Platinum-coated capillary fabricated via Atomic Layer Deposition (ALD) technology as the core for enhanced Raman spectroscopy, marking the first application of ALD in creating such a structure for gas Raman sensing. The noble metal capillary gas Raman probe demonstrates a low detection limit of 55 ppm for CO2 with a 30 s exposure time and good repeatability in multicomponent gas sensing. The capillary exhibits excellent stability, environmental resistance, and a large core diameter, enabling a rapid gas exchange rate and making it suitable for practical applications.

Smart aquaculture analytics: Enhancing shrimp farming in Bangladesh through real-time IoT monitoring and predictive machine learning analysis.

Water quality is a critical factor in shrimp farming, and the success of shrimp production is closely tied to the overall condition of the water. Challenges such as rapid population growth, environmental pollution, and global warming have led to a decline in fisheries production, particularly in the freshwater shrimp sector. This study addresses these challenges by monitoring multiple water parameters in shrimp farms, including pH, temperature, TDS, EC, and salinity. Traditional manual monitoring systems are known to be cumbersome, time-consuming, and lacking real-time capabilities. Consequently, a continuous and automated monitoring system becomes imperative for efficient and real-time metrics handling. This study introduces a real-time freshwater shrimp (locally named Galda, i.e., Macrobrachium Rosenbergii) farm monitoring system. The proposed system incorporates technologies such as microcontroller-based physical devices, IoT, cloud storage with service, machine learning models, and web applications. This integrated system enables users to remotely monitor shrimp farms and receive alerts when water parameters fall outside the optimal range. The physical implementation involves a set of sensors for collecting data on water metrics in shrimp farms. Regression analysis is employed for predicting next-day values, and a newly developed decision-based algorithm classifies shrimp production levels into low, medium, and maximum categories using six well-known classification algorithms. The system demonstrates a high success rate for next-day predictions (r2 of 0.94) by multiple linear regression, and the accuracy in classifying shrimp production is 97.84 % by Random Forest. Additionally, a 'Smart Aquaculture Analytics' web application has been developed, offering features such as real-time dashboards, historical data visualization, prediction and classification tools, and automated notifications to farmers in Bangladesh.

Evaluation of a self-monitoring protocol for assessing soot and polycyclic aromatic hydrocarbon exposure among chimney sweeps.

Traditional methods for measuring chemical exposure have challenges in terms of obtaining sufficient data; therefore, improved methods for better assessing occupational exposure are needed. One possible approach to mitigate these challenges is to use self-monitoring methods such as sensors, diaries, or biomarkers. In the present study, a self-monitored method for measuring soot exposure, which included real-time air monitoring, a work diary, and the collection of urine samples, was evaluated. To validate the method, exposure measurements during the workday and diary entries were compared with velocities calculated from GPS tracking and the expected polycyclic aromatic hydrocarbon (PAH) metabolite patterns in urine. The method was applied with chimney sweeps, an occupational group at a high risk of many severe health outcomes and for whom effective control measures for reducing exposure are needed. In the study, 20 chimney sweeps followed a self-monitoring protocol for 8 consecutive workdays. Personal exposure to soot was measured as black carbon (BC) using micro-aethalometers. A diary was used to record the work tasks performed, and urine samples were collected and analysed for PAH metabolites. From the expected 160 full day measurements, 146 (91%) BC measurements and 149 (93%) diaries were collected. From the expected 320 urine samples, 304 (95%) were collected. The tasks noted in the diaries overlapped with information obtained from the GPS tracking of the chimney sweeps, which covered 96% of the measurement time. The PAH metabolites in urine increased during the work week. Factors believed to have positively influenced the sample collection and task documentation were the highly motivated participants and the continuous presence of trained occupational hygiene professionals during the planning of the study and throughout the measurement stage, during which they were available to inform, instruct, and address questions. In conclusion, the self-monitored protocol used in this study with chimney sweeps is a valuable and valid method that can be used to collect larger numbers of samples. This is especially valuable for occupations in which the employees are working independently and the exposure is difficult to monitor with traditional occupational hygiene methods.

Nanocomposite Hydrogel for Real-Time Wound Status Monitoring and Comprehensive Treatment.

Current skin sensors or wound dressings fall short in addressing the complexities and challenges encountered in real-world scenarios, lacking adequate capability to facilitate wound repair. The advancement of methodologies enabling early diagnosis, real-time monitoring, and active regulation of drug delivery for timely comprehensive treatment holds paramount significance for complex chronic wounds. In this study, a nanocomposite hydrogel is devised for real-time monitoring of wound condition and comprehensive treatment. Tannins and siRNA containing matrix metalloproteinase-9 gene siRNA interference are self-assembled to construct a degradable nanogel and modified with bovine serum albumin. The nanogel and pH indicator are encapsulated within a dual-crosslinking hydrogel synthesized with norbornene dianhydride-modified paramylon. The hydrogel exhibited excellent shape adaptability due to borate bonding, and the click polymerization reaction led to rapid in situ curing of the hydrogel. The system not only monitors pH, temperature, wound exudate alterations, and peristalsis during wound healing but also exhibits hemostatic, antimicrobial, anti-inflammatory, and antioxidant properties, modulates macrophage polarization, and facilitates vascular tissue regeneration. This therapeutic approach, which integrates the monitoring of pathological parameters with comprehensive treatment, is anticipated to address the clinical issues and challenges associated with chronic diabetic wounds and infected wounds, offering broad prospects for application.

TRPA1 nanovesicle-conjugated receptonics for rapid biocide screening.

Although biocides are important materials in modern society and help protect human health and the environment, increasing exposure to combined biocides can cause severe side effects in the human body, such as lung fibrosis. In this study, we developed a receptonics system to screen for biocides in combined household chemical products based on biocides. The system contains transient receptor potential ankyrin 1 (TRPA1) nanovesicles (NVs) to sense biocides based on pain receptors and a side-gated field-effect transistor (SGFET) using a single-layer graphene (SLG) micropattern channel. The binding affinities between the TRPA1 receptor and the various biocides were estimated by performing biosimulation and using a calcium ion (Ca2+) assay, and the sensitivity of the system was compared with that of TRPA1 NV receptonics systems. Based on the results of the TRPA1 NV receptonics system, the antagonistic and potentiation effects of combined biocides and household chemical products depended on the concentration. Finally, the TRPA1 NV receptonics system was applied to screen for biocides in real products, and its performance was successful. Based on these results, the TRPA1 NV receptonics system can be utilized to perform risk evaluations and identify biocides in a simple and rapid manner.

Regulating intermolecular hydrogen bonding to construct solvation-induced emission shift quinoline derivatives for real-time monitoring of water content in organic solvents.

Water constitutes the most prevalent impurity in organic solvents, exerting significant influence on chemical reactions and potentially leading to fires and explosions, even in minute quantities. Thus, the development of convenient, rapid, and cost effective fluorescent probes for real-time monitoring of water content in organic solvents is imperative. Although some fluorescent materials have been synthesized for this purpose, most suffer from laborious preparation processes and poor cycling performance, constraining their practical application. This study investigates the impact of hydrogen bonding on the aggregation-induced emission (AIE) properties of quinoline derivatives, leveraging quinoline as the foundational scaffold and its nitrogen atom as the hydrogen bond acceptor. Research findings elucidate that intermolecular hydrogen bonding of quinoline is the primary determinant of their AIE behaviors. By harnessing the phenomenon of water molecules forming intermolecular hydrogen bonds with quinoline nitrogen atoms, we devised a straightforward and rapid method to fabricate a fluorescent test paper for real-time monitoring of water content in organic solvents. Experimental results demonstrate that even minute changes in water content, down to concentrations as low as 0.5 % by volume in organic solvents, can induce fluorescence changes in the test paper, which also exhibits favorable cycling performance. This study not only explores the influence of hydrogen bonds on the AIE properties of quinoline derivatives but also pioneers the development of a cost-effective, rapid, and recyclable test paper for real-time monitoring of water content fluctuations in organic solvents.

Self-Indicating Polymer Prodrug Nanoparticles for pH-Responsive Drug Delivery in Cancer Cells and Real-Time Monitoring of Drug Release.

Amphiphilic self-indicating and responsive polymer-based prodrugs have generated much interest as potential stimuli-responsive intelligent drug delivery systems (DDS) due to their ability to selectively deliver drugs to the cancer cells and to monitor real-time cellular uptake of the drug by imaging technique(s). In this direction, we have synthesized a new pH-responsive N-vinyl-2-pyrrolidone and coumarin-based fluorescent self-indicating polymeric prodrug (SIPD), poly(NVP)-b-poly(FPA.DOX-r-FPA-r-CA). This block copolymer prodrug self-assembled into stable micellar nanoparticles under physiological conditions that reduced undesirable drug leakage to normal cells but resulted in the release of the anticancer drug doxorubicin (DOX) in cancer cells because of acidic pH-induced cleavage of imine bonds between DOX and the copolymer. While the polymer was found to be highly biocompatible with both normal (HEK-293) cells and cancer (MCF-7) cells even at high concentrations by MTT assay, the polymer prodrug nanoparticles showed toxicity even higher than that of free DOX in cancer cells. Phase contrast microscopy also depicted the cytotoxic effects of the nanoparticles on cancer cells. The coumarin units present in the polymer served as a fluorescence resonance energy transfer (FRET) pair with the covalently attached DOX molecules, which was established by steady-state and time-resolved fluorescence spectroscopy. Furthermore, confocal microscopy results confirmed the FRET phenomenon, as the fluorescence intensity of coumarin in the micellar nanoparticles remained quenched initially in MCF-7 cells but recovered with time as the DOX molecules were released and gradually shifted toward the targeted nucleus. All of these studies implied that the synthesized prodrug nanoparticles may provide another viable option for delivering chemotherapeutic drugs into cancer cells with a capability of real-time monitoring of drug release.

Treatment outcome of localized prostate cancer using transperineal ultrasound image-guided radiotherapy.

BACKGROUND: We report the results of a retrospective analysis of localized prostate cancer (LPCa) treated with transperineal ultrasound image-guided radiotherapy (TPUS-IGRT). METHODS: A total of 124 patients (median age: 74 y, 46-84 y) with LPCa who underwent TPUS-IGRT (Clarity Autoscan system; CAS, Elekta; Stockholm, Sweden) between April 2016 and October 2021 for curative/after hormone induction were enrolled. The number of patients by risk (National Comprehensive Cancer Network 2019) was 7, 25, 42, and 50 for low (LR), good intermediate (good IR), poor intermediate (poor IR), and high (HR)/very high (VHR), respectively. Ninety-five patients were given neoadjuvant hormonal therapy. The planning target volume margin setting was 3 mm for rectal in most cases, 5-7 mm for superior/inferior, and 5 mm for anterior/right/left. The principle prescribed dose is 74 Gy (LR), 76 Gy (good IR), and 76-78 Gy (poor IR or above). CAS was equipped with a real-time prostate intrafraction monitoring (RTPIFM) system. When a displacement of 2-3 mm or more was detected, irradiation was paused, and the patients were placed on standby for prostate reinstatement/recorrection. Of the 3135 fractions in 85 patients for whom RTPIFM was performed, 1008 fractions (32.1%) were recorrected at least once after starting irradiation. RESULTS: A total of 123 patients completed the radiotherapy course. The 5-year overall survival rate was 95.9%. The 5-year biological prostate-specific antigen relapse-free survival rate (bPFS) was 100% for LR, 92.9% for intermediate IR, and 93.2% for HR/VHR (Phoenix method). The 5-year late toxicity rate of Grade 2+ was 7.4% for genitourinary (GU) and 6.5% for gastrointestinal (GI) organs. Comparing the ≤ 76 Gy group to the 78 Gy group for both GU and GI organs, the incidence was higher in the 78 Gy group for both groups. CONCLUSION: These results suggest that TPUS-IGRT is well tolerated, as the bPFS and incidence of late toxicity are almost comparable to those reported by other sources of image-guided radiotherapy.

A bright red fluorescent genetically encoded sensor for lactate imaging.

Lactate plays a crucial role in energy metabolism and greatly impacts protein activities, exerting diverse physiological and pathological effects. Therefore, convenient lactate assays for tracking spatiotemporal dynamics in living cells are desirable. In this paper, we engineered and optimized a red fluorescent protein sensor for l-lactate named FiLa-Red. This indicator exhibited a maximal fluorescence change of 730 % and an apparent dissociation constant (Kd) of approximately 460 µM. By utilizing FiLa-Red and other sensors, we monitored energy metabolism in a multiplex manner by simultaneously tracking lactate and NAD+/NADH abundance in the cytoplasm, nucleus, and mitochondria. The FiLa-Red sensor is expected to be a useful tool for performing metabolic analysis in vitro, in living cells and in vivo.

Charge Transfer Mechanisms of Adaptive Multicomponent Solutions at Solid-Liquid Interfaces for Real-Time Coolant State Monitoring.

Charge-transfer mechanisms in adaptive multicomponent solutions at liquid-solid interfaces with triboelectric probes are crucial for understanding chemistry dynamics. However, liquid-solid charge transfer becomes unpredictable, due to the components or interactions in solutions, restricting its potential application for precise monitoring of liquid environments. This study utilizes triboelectric probes to investigate the charge transfer of chemicals, applying this approach to real-time coolant state monitoring. Analysis of electrical signal dynamics induced by ethylene glycol and its oxidation byproduct, oxalic acid, in ethylene glycol solutions reveals that hydrogen bond and ion adsorption diminishes the efficiency of electron transfer at the liquid-solid interface. These findings promote the engineering of the triboelectric probe that enhances coolant quality with remarkable sensitivity (detection limit: 0.0001%) and a broad freezing point operational range (0 to -49 °C). This work advances the precise control of the charge dynamics and demonstrates the potential of triboelectric probes for interdisciplinary applications.