Lung-protective ventilation is a cornerstone of modern mechanical ventilation, yet real-world adherence to lung-protective targets remains suboptimal. While previous studies have established the physiological benefits of low tidal volume and driving pressure, clinical implementation is hindered by limited monitoring granularity and lack of real-time actionable feedback. This trial aims to evaluate whether a real-time, cloud-based algorithmic feedback platform can improve lung-protective ventilation delivery and contribute to better clinical outcomes in mechanically ventilated patients with ARDS. This multicentre, parallel-group, open-label randomised controlled trial will enrol 208 adult mechanically ventilated ICU patients with ARDS from nine adult ICUs across tertiary academic hospitals and regional referral centres in multiple provinces and municipalities in mainland China. Participants will be randomly assigned in blocks to receive either standard monitoring (Control group) or real-time respiratory mechanics feedback through a cloud-based platform (Intervention group). The intervention group will receive real-time alerts for lasting 72 h and ventilator reports every 24 h, integrating tidal volume, plateau pressure, driving pressure, mechanical power, and detected patient-ventilator asynchrony events. The primary outcome is the lung-protective ventilation achievement rate, defined as compliance with VT < 8 mL/kg predicted body weight, driving pressure < 15 cmH₂O, plateau pressure < 30 cmH₂O, and mechanical power < 17 J/min during the first 72 h after randomisation. Secondary outcomes include ventilator-free days at day 28, ICU length of stay, ventilator-associated complications, inflammatory biomarkers, clinician satisfaction, and predefined safety outcomes, including severe hypoxemia, severe hypercapnia/acidemia, barotrauma, and hemodynamic instability temporally associated with ventilator adjustments. This study is, to our knowledge, among the first multicentre randomised controlled trials to evaluate a real-time algorithmic feedback platform designed to enhance lung-protective ventilation. The intervention is designed to provide continuous bedside feedback on ventilation mechanics and may enable more timely and standardised clinical adjustments, with the potential to facilitate lung-protective ventilation delivery. Triaiontl registration ClinicalTrials.gov Identifier NCT07307066 (Registration Date: 2025/12/02).
In sentences such as "John remembered the boy took some time to rest", the locally ambiguous noun phrase (NP) "the boy" is initially parsed as the direct object of the matrix verb "remembered" (the object analysis). When the embedded verb "took" is encountered, the NP is revised as the subject of the embedded clause (the subject analysis). Open questions are how the real-time resolution of this complement ambiguity is influenced by semantic/categorial constraints (i.e., whether the locally ambiguous NP is a semantically/categorially appropriate object of the matrix verb) and selectional frequency (i.e., the frequency with which the matrix verb takes a direct object NP). The present study addressed these questions and also examined whether temporal adjuncts, which can bias parsing towards the object analysis (e.g., "John remembered the boy after…"), influence real-time ambiguity resolution. The results showed that semantic/categorial constraints and selectional frequency drive the processor towards the subject analysis before embedded-verb disambiguation. However, temporal adjuncts gradually increased in influence and ultimately overrode these biases during processing. The observed parsing process was also simulated within an interactive constraint-based framework. Together, the experimental and simulation results suggest that real-time sentence processing is dynamically shaped by multiple competing biases.
Solitary fibrous tumor of liver (SFTL) is a rare mesenchymal neoplasm. Due to its low incidence and atypical clinical presentation, preoperative diagnosis is often challenging, making imaging features critical for its identification. Contrast-enhanced ultrasound (CEUS), a dynamic imaging technique, has been demonstrated to provide high diagnostic accuracy for hepatic lesions. This study reports the CEUS characteristics of a percutaneous biopsy-confirmed SFTL in an adult patient. The findings included peripheral hyperenhancement during the arterial phase, progressive centripetal filling with partial perfusion defects, and early washout commencing in the portal venous phase, which became more pronounced in the delayed phase.
The Internet of Medical Things (IoMT) enables sophisticated medical devices, but it also poses significant challenges in terms of data privacy, real-time processing, and energy efficiency for edge devices with limited resources. In this paper, we propose a hierarchical framework for intelligent and secure IoMT-based healthcare monitoring. At the sensor nodes, Federated Variational Mode Decomposition (VMD) is used to decompose physiological signals and locally extract high-fidelity features, ensuring data privacy. To overcome the computational limitations of microcontroller- based sensor nodes, a SparseBonsai neural network is designed for real-time classification of medical signals on the sensor nodes. A centralized orchestration layer, controlled by a Proximal Policy Optimization (PPO) reinforcement learning agent, makes dynamic decisions on whether to queue data for low-latency processing at the edge server or offload to the cloud, depending on data severity, network conditions, and battery level. To further improve energy efficiency, an advanced Sha-Dragon (Shannon-Entropy Dragonfly) optimization algorithm is proposed for resource and transmission power allocation in the IoT network. For security, a dual-layer approach is adopted: ASCON v1.2 lightweight authenticated encryption is used to secure node-to-edge communications, and a WireGuard VPN with ChaCha20-Poly1305 encryption protects data in transit to the cloud. Experimental validation on a Raspberry Pi 5 testbed with a cloud-connected laptop shows that the proposed system achieves a significant reduction in latency for critical alerts and improves the battery life of IoT nodes (8.5 days) compared to the conventional non-adaptive offloading approach. The results confirm the effectiveness of the proposed framework to facilitate energy-efficient, privacy-preserving, and real-time healthcare monitoring in IoMT.
Real-time electrochemical measurements of ionic fluxes and bioelectric signals are set to refine cancer diagnosis and longitudinal follow-ups with the goal of targeting electrophysiological features of clinically useful matrices. Nanoengineered electrodes translate biomolecules and ions into robust electrochemical signals for both laboratory workflows and point-of-care testing. The translational aim is to integrate electrochemical sensing into coherent, physiology-grounded readouts that resolve tumor ion channel dysfunction, membrane depolarization, pericellular acidification, and redox imbalance. Realizing this vision now depends on practical engineering and clinical integration. To advance this ongoing development, researchers are building stable, disposable, portable, and miniaturized electrochemical platforms that couple detection with microfluidics to enrich tumor cells, vesicles, and bio ionic markers for multiplexed cancer measurements. The integration of wearable and implantable systems with machine learning and patient-specific digital twins will enable real-time maps of tumor electrophysiology and model-driven forecasts of treatment response. This perspective outlines a translational roadmap for electrochemical detection of bioelectric biomarkers in cancer, wherein biomarker classes are systematically mapped to corresponding transduction strategies and mechanistic fidelity is reconciled with practical assay design constraints to identify key challenges and opportunities for advancing these technologies from foundational research to validated clinical implementation in precision oncology.
The rapid advancements in nanotechnology and medical imaging have positioned magnetic resonance imaging (MRI)-guided magnetic micro/nanorobots (MNRs) as a promising platform for integrated diagnosis and treatment of neurological diseases. This review highlights recent progress in MNR development, focusing on fabrication techniques, driving mechanisms, multimodal imaging integration, AI-enabled navigation, blood-brain barrier (BBB) penetration, and disease-specific applications. We analyze biomimetic designs and functional materials for MNRs, including pH- and ROS-responsive degradable materials, liquid metals, and multifunctional coatings.We further examine MRI-based navigation, emphasizing gradient and rotational field control, closed-loop kinematics, and MRI system compatibility. The review also explores AI-assisted multimodal imaging, including MRI, PET, photoacoustic, and fluorescence techniques, alongside the role of deep learning and digital twin models in real-time tracking and path optimization. In addressing the critical challenge of BBB penetration, we review both conventional and emerging strategies. Finally, we assess preclinical findings on the application of MNRs in brain tumors, cerebrovascular diseases, and neurodegenerative disorders. We further highlight how MNR systems improve therapeutic outcomes by enhancing local drug concentrations, enabling spatiotemporally controlled release, and providing real-time imaging feedback, thereby addressing key failure modes of conventional systemic therapies. The review concludes with a critical assessment of translational challenges and a roadmap toward intelligent, personalized neurotherapeutics.
The production of bacterial-based vaccines depends fundamentally on advanced bioreactor technologies, which are continuously evolving to meet rising demands for efficiency, quality, and scalability. In this review, several key challenges that constrain the translational impact of bioreactor technologies for bacterial vaccines were identified. First, there is notable variability in bioreactor performance across different bacterial strains and vaccine targets, which undermines the generalizability of process designs. Second, the understanding of how scale-up affects yield, product quality, immunogenicity, and regulatory readiness remains incomplete. It limits confidence in translating lab-scale results to manufacturing environments. Third, real-time analytics and decision-support tools are not sufficiently integrated into control strategies. This leads to fragmented data-to-decision workflows. The review provides concrete, actionable directions to address these challenges. Emerging trends such as real-time monitoring, simulation tools, data-driven process intensification, and modular, single-use platforms are discussed for their potential to address these limitations. The optimized bioreactor design, precise operational control, and sustainable practices are emphasized. It is also illustrated how multidisciplinary collaborations can tailor bioreactor configurations to specific bacterial strains and vaccine targets that deliver reliable scalability.
Peroxynitrite (ONOO-) and cysteine (Cys) are pivotal components of reactive nitrogen and sulfur species that together shape a complex redox regulatory network in living systems. High-resolution, real-time imaging of these species is essential for unraveling their roles in mitochondrial redox dynamics. However, the development of fluorescent probes capable of simultaneously detecting ONOO- and Cys with high fidelity remains limited. In this work, we introduce a single-excitation, dual-response fluorescent probe system (probes A and B) that enables synchronous visualization of ONOO- and Cys. The probe design integrates two orthogonal sensing chemistries: ONOO- is selectively recognized via the oxidation of a naphthylimide-borate unit, while Cys is detected through nucleophilic substitution with a benzothiol-modified tetrahydro-acridine conjugate salt. Incorporation of a long-chain linker effectively suppresses undesired FRET, and the large Stokes shift of the Cys-responsive unit ensures clean spectral separation. As a result, probe B exhibits strong ONOO- emission at 550 nm and distinct Cys emission at 652 nm under a single 460 nm excitation source, with both fluorescence channels operating independently and without spectral crosstalk. Probe B demonstrates high sensitivity, excellent anti-interference capability, and precise mitochondrial localization. Dual-channel imaging in living cells confirms its ability to simultaneously monitor endogenous ONOO- and Cys. Importantly, the probe enables real-time visualization of dynamic redox fluctuations during inflammatory responses and tumor ferroptosis. Overall, this work presents a robust dual-channel fluorescent imaging platform for the simultaneous detection of ONOO- and Cys, offering a powerful tool for dissecting mitochondrial redox dynamics in health and disease.
Routine collection of electronic patient-reported outcome measures (ePROMs) enhances symptom monitoring and improves outcomes in cancer care. We evaluated published reports of real-world ePROM implementation in oncology, focusing on approaches to implementation, types of ePROMs used, completion rates, factors associated with ePROM completion, and impacts on patient and health care process/system-level outcomes. We also aimed to propose a classification of ePROM programmes. We systematically searched Medline, Embase, CINAHL Complete, and Scopus for articles describing routine ePROM implementation and evaluation. Data charting captured characteristics of the study, study population, characteristics of routinely collected ePROMs, and evaluation of their routine use. We also present a new classification of ePROM programmes into three levels: asynchronous, clinician-supervised, and real-time clinician-supervised, based on clinical pathway integration. Of 10 384 identified manuscripts, 50 met the inclusion criteria, reporting on 39 real-world ePROM programmes. Nearly all ePROM programmes (97.4%) collected data on symptom burden. The median overall completion rate was 61% (interquartile range 47%-76%), with substantial variability over time and across hospitals. Completion was associated with sociodemographic, clinical, and treatment-related factors. Most ePROM programmes (61.5%) were classified as asynchronous models, with five (12.8%) clinician-supervised models and three (7.7%) real-time clinician-supervised models, while seven (17.9%) lacked sufficient detail. The most frequently reported outcomes were fewer hospitalisations or emergency department visits (10.3%) at the patient level and improved symptom identification (17.9%) at the process/system level. ePROMs are increasingly embedded in routine oncology care, demonstrating benefits at both patient and system levels. However, most current programmes adapt asynchronous models with limited rapid clinician interaction.
Extended Reality (XR) technologies offer transformative potential for language education, yet current platforms largely neglect the accessibility needs of deaf and hard-of-hearing individuals. Existing solutions typically operate in single-language environments and lack integrated support for sign language and multimodal communication. There is a critical need for inclusive platforms that serve both deaf and hearing learners through cross-modal AI services embedded in immersive environments. This study presents a modular platform integrating six AI services: speech-to-text transcription (OpenAI Whisper), multilingual translation (Meta NLLB), text-to-speech synthesis (AWS Polly), sentiment analysis (RoBERTa), session summarisation (flan-t5-base-samsum), and International Sign (IS) translation via Google MediaPipe. An IS dataset of 750 gesture videos was processed to extract hand landmark coordinates mapped to 3D avatar animations within a Unity-based VR environment on Meta Quest 3 headsets. The system was validated through technical benchmarking of AI service performance, including comparative evaluation of text-to-speech services and multilingual translation models (NLLB-200 and EuroLLM 1.7B variants), load testing to assess platform. scalability, and end-to-end pipeline latency measurement for both the hearing and the deaf user pathways. The educational scenario was additionally evaluated in a companion pilot study, 50 which shares the same underlying AI services and provides complementary user-perception evidence. Technical benchmarking confirmed the platform's viability for real-time XR deployment. TTS benchmarking confirmed AWS Polly's lowest latency (50-100 ms first byte) at competitive cost. The EuroLLM 1.7B Instruct model achieved a BLEU score of 84.34, outperforming NLLB's 79.25. Load testing with 1,000 simulated concurrent users demonstrated average response times under 800 milliseconds with no critical failures. Avatar animation latency for IS sign rendering remained consistently under 300 milliseconds. End-to-end pipeline latency averaged 2.05 ± 0.31 s for the hearing pathway and 2.32 ± 0.34 s for the deaf (IS) pathway, both within accepted thresholds for conversational educational use. The companion pilot (N = 10) reported a mean overall experience rating of 4.6/5.0, 92% user satisfaction and unanimous (100%) demand for expanded language and sign-language support. 50. The results presented in this study focus on the technical feasibility of integrating cross-modal AI services within XR environments for accessible, multilingual language learning. The modular architecture enables independent scaling and adaptation to diverse contexts, laying the groundwork for equitable educational solutions aligned with EU digital accessibility objectives. Learning a new language can be challenging, and it is even more difficult for deaf individuals who rely on sign language. This study addresses the challenge by creating a virtual reality (VR) learning environment where a digital 3D character (avatar) can speak, translate, and perform sign language in real time. The system uses several artificial intelligence tools working together: one that converts speech into text, another that translates text between multiple languages, a third that converts text back into spoken language, and a fourth that translates text into International Sign Language gestures performed by the avatar. Users wear a VR headset and interact with the avatar in a virtual classroom where they can select their preferred language and receive immediate translations in both spoken and signed forms. The system’s technical performance was validated through benchmarking of AI translation models, text-to-speech services, end-to-end pipeline latency measurement, and scalability testing, confirming its suitability for real-time educational applications. Complementary user feedback gathered in a companion pilot study, which uses the same underlying AI services, is cross-referenced where relevant. 50 This research is a step toward creating virtual learning environments where language barriers and hearing limitations no longer prevent people from accessing education.
Autochthonous human leishmaniasis caused by Leishmania (Mundinia) martiniquensis has been increasingly reported in Northern Thailand. However, the potential animal reservoirs for this parasite remain unidentified. Herein, we report the first documented case of canine cutaneous leishmaniasis caused by L. martiniquensis in a dog from Chiang Mai Province. The patient presented with a firm, non-ulcerative nodule (0.3 × 0.4 cm) on the concave surface of the right ear pinna. Fine-needle aspiration cytology revealed protozoan amastigotes, suggestive of Leishmania infection. The parasite was successfully isolated and cultivated in Grace's insect medium, supplemented with 30% fetal bovine serum. Molecular confirmation was performed using nested polymerase chain reaction (nPCR), real-time quantitative PCR (qPCR), sequencing, and phylogenetic analysis of the internal transcribed spacer 1 region, which classified the isolate as Leishmania martiniquensis. Comprehensive clinical evaluations, including complete blood count, serum biochemistry, and abdominal imaging (radiography and ultrasonography), revealed no evidence of systemic involvement consistent with visceral leishmaniasis. However, persistent polycythemia and mild protein alterations were observed during follow-up. Successful clinical resolution of the cutaneous lesion was achieved after four consecutive weekly intralesional amphotericin B treatments. This report demonstrates that dogs are vulnerable to cutaneous L. martiniquensis infections. A comprehensive review of clinical manifestations of L. martiniquensis infection in animals worldwide is presented, emphasizing the need for further studies on animal infection, vector transmission, and epidemiology within a One Health framework.
Microalgae have emerged as a promising bioresource for the integrated treatment of effluent, the production of biomass, and the mitigation of carbon emissions, thereby contributing to the Sustainable Development Goals (SDGs 7 and 13). This review summarizes the most recent developments in the management of wastewater using microalgae, with a particular focus on resource recovery and process integration. Nevertheless, challenges such as oxygen accumulation, biofouling, high energy demand, and scale-up constraints persist, despite the fact that configurations such as tubular, flat-panel, and bubble-column reactors offer flexibility across scales. Additionally, the topic of the integration of artificial intelligence and machine learning is addressed in order to address process constraints by means of predictive modeling, real-time optimization, and enhanced strain and process selection. In conclusion, this review offers a concentrated viewpoint on the advancement of sustainable microalgae systems by integrating technological and biological innovations.
This study evaluated the feasibility of ultrasound (US) parameters for predicting the transition from acute kidney injury (AKI) to chronic kidney disease (CKD) and assessing the therapeutic response to 17-DMAG, a fibrosis-mitigating agent, in a murine unilateral ischemia-reperfusion injury (UIRI) model. Male C57BL/6 mice were assigned to sham (n=16) or UIRI (n=24) groups, with half of the UIRI mice receiving 17-DMAG (20 mg/kg intraperitoneally, three times weekly). Serial US examinations were performed on postoperative days (PODs) 3 and 8 to evaluate morphological parameters (kidney size and parenchymal thickness [PT]), vascular parameters (resistive index [RI] and vascular index [VI] derived from microvascular imaging [MVI]), and tissue stiffness assessed by shear-wave speed (SWS). Pathologic fibrosis was defined as a Sirius red-positive area >4%. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. At the early stage (POD 3), vascular parameters (VI and RI) demonstrated high diagnostic performance for predicting fibrosis progression (area under the receiver operating characteristic curve, 0.948 and 0.890, respectively), with VI serving as the only significant predictor of early treatment response to 17-DMAG. At POD 8, all parameters showed significant diagnostic performance for predicting fibrosis progression. However, for treatment response, only kidney size, PT, and RI demonstrated significant ROC performance, whereas VI and SWS did not reach statistical significance. These findings reflect the temporal transition from early functional microvascular compromise to established structural remodeling and parenchymal atrophy. RI and VI are promising noninvasive surrogate markers for predicting the AKI-to-CKD transition, and VI may be useful for assessing early responses to 17-DMAG treatment. Whereas conventional US parameters identify late-stage structural remodeling, MVI provides a critical diagnostic window during the acute phase by detecting early microvascular compromise. These findings highlight the potential utility of MVI for real-time monitoring of AKI progression and anti-fibrotic treatment responses in clinical practice.
Children undergoing corrective surgery for congenital heart disease (CHD) are at risk of perioperative hemodynamic instability and low cardiac output syndrome (LCOS). Conventional monitoring relies on static parameters that may inadequately reflect real-time cardiac performance. Electrical cardiometry (EC) provides continuous, noninvasive assessment of cardiac output and related indices. This study evaluated whether EC-guided perioperative management improves early postoperative outcomes compared with conventional monitoring in pediatric CHD surgery. In this prospective randomized controlled trial, 60 children (0-15 years) undergoing elective corrective CHD surgery were randomized to EC-guided management (Group 1, n = 30) or conventional monitoring (Group 2, n = 30). In Group 1, perioperative fluid and vasoactive therapy were guided by EC-derived parameters, including cardiac index (CI), stroke volume, thoracic fluid content, and stroke volume variation. Group 2 was managed using standard clinical and invasive parameters. The primary outcome was the incidence of LCOS within 48 h postoperatively. The secondary outcomes included vasoactive inotropic score (VIS), major adverse cardiac events, and in-hospital mortality. Baseline characteristics were comparable. Group 1 demonstrated significantly higher mean arterial pressure (MAP) at 8, 16, and 24 h postoperatively, faster lactate clearance, and lower VIS during the early postoperative period. CI correlated positively with MAP and urine output and inversely with lactate levels. No mortality occurred. EC-guided perioperative management aids in achieving early postoperative hemodynamic stability and metabolic recovery in children undergoing corrective CHD surgery and represents a valuable noninvasive adjunct to conventional monitoring.
Healthcare organizations increasingly rely on digital integration to strengthen pharmaceutical supply chain governance. At the time of this study, Qatar's Primary Health Care Corporation (PHCC) operated a decentralized pharmacy network without a dedicated internal Enterprise Resource Planning (ERP) system, resulting in fragmented procurement data and limited enterprise-level visibility. This study aimed to design and implement an enterprise-level data integration and analytics solution to enhance procurement transparency, inventory visibility, and data-informed decision-making across PHCC's pharmacy network. Guided by a Design Science Research (DSR) methodology, a centralized Enterprise Data Warehouse (EDW) was developed by integrating procurement data extracted from the supplier's ERP system. A Tableau-based analytics dashboard featuring key performance indicators (KPIs) was designed and deployed across 32 pharmacies to support operational monitoring and governance. The implemented solution enabled real-time visibility of pharmaceutical procurement and inventory performance. Descriptive operational indicators showed a reduction in back-order rates from 6.7% to 4.5% in 2024. Inventory indicators demonstrated an increase in average medication shelf life from 551 to 568 days (3%) and an improved expiry profile, with 96% valid inventory and only 3% very short-expiry items compared with historical levels. This case study demonstrates how integrating external supplier ERP data into an enterprise data warehouse can enhance visibility, transparency, and decision support in public healthcare systems lacking an internal ERP. The approach provides a scalable and practical model for strengthening pharmaceutical supply chain governance without large-scale ERP implementation.
The management of advanced low rectal cancer is shaped by an ongoing tension between two surgical philosophies regarding lateral pelvic lymph node (LPLN) disease. Eastern and Western guidelines diverge on whether systematic lateral lymph node dissection (LLND) should accompany total mesorectal excision (TME), reflecting different views of the locoregional behavior of LPLN involvement and different thresholds for accepting the urinary and sexual morbidity that accompanies full sidewall clearance. In this editorial, we argue that intraoperative interrogation of each individual case offers a way to refine, rather than resolve by regional consensus, this geography-driven dichotomy, and that indocyanine green (ICG) near-infrared fluorescence provides a principled means of individualizing the extent of lateral dissection. Peritumoral submucosal injection of ICG enables real-time visualization of lymphatic drainage and intraoperative identification of lateral pelvic sentinel lymph nodes (LPSLNs), which can be biopsied and submitted for frozen section. It is important to distinguish between the two applications of this signal. As an intraoperative visualization adjunct, ICG improves lateral node retrieval, and the supporting comparative evidence is relatively consistent. As a sentinel-based decision tool for safely omitting lateral dissection when the sentinel node is negative, the concept is promising but rests on a smaller and less mature evidence base; it remains investigational and requires validation in larger prospective studies with long-term oncologic and functional outcomes before it can guide the omission of lateral dissection in practice. We outline the operative protocol used in our department, extended from sentinel node mapping for gynecological malignancies, and place it in the context of contemporary systematic, propensity-matched, and prospective sentinel biopsy series. In our view, fluorescence-assisted selective LLND may serve as a pragmatic bridge between existing paradigms, preserving the oncologic intent of Japanese-style lateral clearance while aligning with Western priorities of minimizing unnecessary morbidity through tailored, image-guided surgery.
Shear wave elastography (SWE) is an established technique for evaluating thyroid, liver, and breast pathologies. This non-invasive, real-time diagnostic method has also shown value for assessing the mechanical properties of skeletal muscle. However, the specific challenges of pelvic floor evaluation remain insufficiently addressed. Although SWE may appear straightforward, it involves considerable technical and analytical complexity and carries a substantial risk of misinterpretation. This review aims to increase awareness of challenges in SWE image acquisition and analysis, potential pitfalls, common artifacts, and other technical considerations relevant to pelvic floor muscle assessment. By addressing these issues, this review is intended to promote quality and consistency in future SWE research and to highlight areas requiring further investigation.
Organ-on-a-Chip (OOC) technology offers a powerful platform for replicating human tissue-specific microenvironments, thereby narrowing the translational gap between conventional biomedical models and actual human physiology. Concurrently, omics technologies deliver comprehensive molecular-level insights into biological systems. This review highlights the transformative potential of integrating OOC platforms with high-throughput omics methodologies. We systematically examine the classification, structural configurations, and engineering principles underlying OOC systems, alongside the defining attributes of key omics domains-genomics, transcriptomics, proteomics, and metabolomics. The convergence of dynamic OOC models with advanced omics technologies enables high-resolution, multi-dimensional analyses across numerous biomedical applications, including drug metabolism, disease mechanisms, environmental toxicity assessments, and host-microbiome interactions. This interdisciplinary integration is driving a paradigm shift in precision and translational medicine. However, several challenges remain to be addressed, such as the development of whole-organ mimetics, adaptation of sample collection techniques, and real-time artificial intelligence-based integration of biosensor data with multi-omics datasets. Addressing these hurdles will be vital for unlocking the full potential of this technological synergy in biomedical science.
Cotton leaf diseases present a major threat to global cotton production, significantly impacting both yield and fiber quality. Traditional diagnostic methods are labor-intensive, time-consuming, and demand highly skilled professionals, making them inefficient for large-scale agricultural applications. Although earlier deep learning -based approaches have shown promising results in identifying cotton leaf diseases such as Bacterial Blight, Fusarium Wilt, and Curl Virus Disease, their performance is often limited by complex preprocessing requirements and insufficient generalization to real-world field conditions. To address these challenges, this study proposes and optimized transfer learning-based model, CLDP-CNN, designed to enhance feature extraction and classification efficiency using pre-trained deep neural networks. This study demonstrates the development of Cotton Leaf Disease Prediction Convolutional Neural Network (CLDP-CNN) automatically, utilizing Transfer Learning (TL) which operates on meticulously prepared datasets. Two distinct datasets were used to train the model: the first consisted of field images from cotton farms, while the second was sourced from Kaggle. The main goal of this research examines how the model performs on real-world field datasets. The CLDP-CNN model has proven highly accurate by attaining 99.78% detection success rates for cotton leaf diseases when processing primary dataset which surpasses its secondary dataset accuracy rate of 99.62%. Both the primary dataset and secondary dataset resulted in high accuracy values for the VGG16 pre-trained model which achieved 99.56% accuracy on the primary dataset and 98.82% on the secondary dataset. A web-based application enhances the capabilities of the CLDP-CNN model by providing real-time updates on the health status of cotton plants. This technology empowers farmers with valuable information, enabling them to take timely protective actions to prevent potential severe yield losses in their cotton crops.
Accurate, real-time estimation of core body temperature (CBT) during physical activity is essential for monitoring heat strain and mitigating the risk of heat-related illness under hot environmental conditions. Although numerous data-driven algorithms using wearable sensors have been proposed, their practical reliability remains unclear due to substantial methodological heterogeneity and the absence of standardized evaluation. This study combined a systematic review with a standardized quantitative benchmark. A total of 38 studies employing non-invasive inputs for CBT estimation were identified. Of these, 14 eligible models, including Kalman filter-based methods, statistical models, and machine-learning approaches, were re-implemented and evaluated under identical preprocessing and evaluation settings using two independent datasets: Dataset 1 (treadmill walking, n = 16 ) and Dataset 2 (cycling, n = 13 ). The benchmark revealed notable differences between originally reported performance and reproduced performance under standardized conditions. For the widely used heart-rate-based extended Kalman filter, the root mean square error (RMSE) increased from typically reported values of ∼ 0.21-0.41  ∘ C to 0.41  ∘ C on Dataset 1 and 0.66  ∘ C on Dataset 2. Incorporating skin temperature improved tracking accuracy in some configurations, but performance gains were highly dependent on measurement site and dataset. Sensitivity for detecting elevated CBT ( ≥ 38.0  ∘ C) varied markedly across methods, particularly for the cycling protocol. In conclusion, no single CBT estimation approach consistently outperformed others across all settings. Heart-rate-only models provided a stable baseline under limited sensing conditions, whereas multimodal approaches offered conditional benefits in more controlled scenarios. This work establishes a standardized benchmark framework to support fair comparison, method selection, and future development of (wearable) CBT estimation technologies.