The precise prediction of hypotension is vital for advancing preemptive patient care strategies. Traditional machine learning approaches, while instrumental in this field, are hampered by their dependence on structured historical data and manual feature extraction techniques. These methods often fall short of recognizing the intricate patterns present in physiological signals. Addressing this limitation, our study introduces an innovative application of deep learning technologies, utilizing a sophisticated end-to-end architecture grounded in XResNet. This architecture is further enhanced by the integration of contrastive learning and a value attention mechanism, specifically tailored to analyze arterial blood pressure (ABP) waveform signals. Our approach improves the performance of hypotension prediction over the existing state-of-theart ABP model [7]. This research represents a step towards optimizing patient care, embodying the next generation of AI-driven healthcare solutions. Through our findings, we demonstrate the promise of deep learning in overcoming the limitations of conventional prediction models, thereby offering an avenue for enhancing patient outcomes in clinical settings.

Maintaining consistent and accurate temperature is critical for the safe and effective storage of vaccines. Traditional monitoring methods often lack real-time capabilities and may not be sensitive enough to detect subtle anomalies. This paper presents a novel deep learning-based system for real-time temperature fault detection in refrigeration systems used for vaccine storage. Our system utilizes a semi-supervised Convolutional Autoencoder (CAE) model deployed on a resource-constrained ESP32 microcontroller. The CAE is trained on real-world temperature sensor data to capture temporal patterns and reconstruct normal temperature profiles. Deviations from the reconstructed profiles are flagged as potential anomalies, enabling real-time fault detection. Evaluation using real-time data demonstrates an impressive 92% accuracy in identifying temperature faults. The system\'s low energy consumption (0.05 watts) and memory usage (1.2 MB) make it suitable for deployment in resource-constrained environments. This work paves the way for improved monitoring and fault detection in refrigeration systems, ultimately contributing to the reliable storage of life-saving vaccines.

Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role in real-time sensing data analysis to provide clinical-grade information for personalized healthcare. To this end, supervised learning and unsupervised learning algorithms have emerged as powerful tools, allowing for the detection of complex patterns and relationships in large, high-dimensional data sets. In this Review, we aim to delineate the latest advancements in machine learning for wearable sensors, focusing on key developments in algorithmic techniques, applications, and the challenges intrinsic to this evolving landscape. Additionally, we highlight the potential of machine-learning approaches to enhance the accuracy, reliability, and interpretability of wearable sensor data and discuss the opportunities and limitations of this emerging field. Ultimately, our work aims to provide a roadmap for future research endeavors in this exciting and rapidly evolving area.

This review explores the transformative impact of AI on drug development and delivery in pharmaceutical sciences, spanning formulation design, real-time monitoring, targeted delivery, and future prospects. The rational design of smart drug carriers, such as AI-optimized liposomes for cancer therapy, optimizes formulations for individual patient needs. AI-driven sensors, exemplified by glucose-monitoring biosensors for diabetics, enable adaptive drug administration, enhancing precision. Despite promises, challenges like biocompatibility, regulations, and ethics persist. Interdisciplinary collaboration and transparent communication are crucial for responsible AI adoption. Anticipated trends include personalized dosage optimization and intelligent nanocarriers. The review underscores AI\'s potential in reshaping pharmaceuticals for patient-centric care while addressing challenges for widespread adoption.

Monitoring and quantifying ATP levels in vivo is essential to understanding its role as a signaling molecule in tumor progression and therapy. Nevertheless, the real-time monitoring and quantitative assessment of lysosomal ATP remains challenging due to the lack of accurate tools in deep tissues. In this study, based on the crosslinking enhanced emission (CEE) effect, we successfully synthesized red carbon dots (R-CDs) with dual emission properties for efficient quantification of intracellular ATP. The R-CDs emit in the near-infrared range and target lysosomes with rapid detection capabilities, rendering them exceptionally well-suited for directly observing and analyzing the dynamics of lysosomal ATP through live cell imaging techniques. Importantly, R-CDs have proven their efficacy in real-time monitoring of drug stimulus-induced fluctuations in endogenous lysosomal ATP concentration and have also been employed for quantifying and distinguishing lysosomal ATP levels among normal and cancer cell lines. These noteworthy findings emphasize the versatility of the R-CD as a valuable imaging tool for elucidating the functional role of lysosomal ATP in drug screening and cancer diagnostics and hold the promise of becoming a reference tool for deepening our understanding of drug mechanisms of action.

For hazardous gas monitoring and non-invasive diagnosis of diabetes using breath analysis, porous foams assembled by Co3O4 nanoparticles were designed as sensing electrode materials to fabricate efficient yttria-stabilized zirconia (YSZ)-based acetone sensors. The sensitivity of the sensors was improved by varying the sintering temperature to regulate the morphology. Compared to other materials sintered at different temperatures, the porous Co3O4 nanofoams sintered at 800 °C exhibited the highest electrochemical catalytic activity during the electrochemical test. The response of the corresponding Co3O4-based sensor to 10 ppm acetone was -77.2 mV and it exhibited fast response and recovery times. Moreover, the fabricated sensor achieved a low detection limit of 0.05 ppm and a high sensitivity of -56 mV/decade in the acetone concentration range of 1-20 ppm. The sensor also exhibited excellent repeatability, acceptable selectivity, good O2/humidity resistance, and long-term stability during continuous measurements for over 30 days. Moreover, the fabricated sensor was used to determine the acetone concentration in the exhaled breaths of patients with diabetic ketosis. The results indicated that it could distinguish between healthy individuals and patients with diabetic ketosis, thereby proving its abilities to diagnose and monitor diabetic ketosis. Based on its excellent sensitivity and exhaled breath measurement results, the developed sensor has broad application prospects.

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.

Hypothermic preservation (HP) is highly desired for the maintenance of the viability of living cell specimens, e.g. rare cells in whole-blood samples or therapeutic cells, in an unfrozen state. However, the extension of the viable preservation time is a challenge because of the multiple injuries suffered by hypothermically preserved cells. Here, based on a dynamic bond crosslinked zwitterionic hydrogel, we established a sensing preservation system that could monitor the levels of reactive oxygen species (ROS) via real-time electronic signals and intelligent control of antioxidant addition, to completely prevent an excess of ROS in the whole-cell specimen. Furthermore, the hydrogel-based system can counter the extracellular-matrix-loss-induced anoikis of living cells. Based on the design aimed at affording protection against two primary HP injuries (i.e. ROS overproduction and anoikis) to cells, this system extended the preservation time of cell specimens under refrigerated conditions to 24 days. After preservation, the use of a mild cell retrieval process guaranteed the activity of the preserved living cells. This work not only possesses the potential to facilitate intelligent cell-based clinical applications, but also paves the way for the preparation of living materials that can host programmed cells with long-term survival. STATEMENT OF SIGNIFICANCE: An intelligent system based on a zwitterionic sensing hydrogel is established, which can afford ultra-long hypothermic cell-preservation times of up to 24 days. The system enables the real-time monitoring of ROS overproduction and intelligent antioxidant addition, because of the merging of the smart hydrogel with a computer intelligent detection and control system. Furthermore, the automatic addition of an antioxidant according to the ROS-signal changes produced by the ZBA hydrogel effectively prevented HP lesions, including ROS over-production and ECM loss, in the preserved living cells. Subsequently, the system could also be gently dissociated, to retrieve the preserved cells. This work provides a solution for the real-time monitoring and long-term HP of living specimens, which holds the promise of benefiting cell-based medicine and the development of genetically programmed cell-based living materials.

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.

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.
CONCLUSIONS: 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.