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We report a novel cancer-targeted nanomedicine platform for imaging and prospect for future treatment of unresected ovarian cancer tumors by intraoperative multimodal phototherapy. To develop the required theranostic system, novel low-oxygen graphene nanosheets were chemically modified with polypropylenimine dendrimers loaded with phthalocyanine (Pc) as a photosensitizer. Such a molecular design prevents fluorescence quenching of the Pc by graphene nanosheets, providing the possibility of fluorescence imaging. Furthermore, the developed nanoplatform was conjugated with poly(ethylene glycol), to improve biocompatibility, and with luteinizing hormone-releasing hormone (LHRH) peptide, for tumor-targeted delivery. Notably, a low-power near-infrared (NIR) irradiation of single wavelength was used for both heat generation by the graphene nanosheets (photothermal therapy [PTT]) and for reactive oxygen species (ROS)-production by Pc (photodynamic therapy [PDT]). The combinatorial phototherapy resulted in an enhanced destruction of ovarian cancer cells, with a killing efficacy of 90%-95% at low Pc and low-oxygen graphene dosages, presumably conferring cytotoxicity to the synergistic effects of generated ROS and mild hyperthermia. An animal study confirmed that Pc loaded into the nanoplatform can be employed as a NIR fluorescence agent for imaging-guided drug delivery. Hence, the newly developed Pc-graphene nanoplatform has the significant potential as an effective NIR theranostic probe for imaging and combinatorial phototherapy.

Colonoscopies provide essential diagnostic capabilities, helping detect colorectal cancer (CRC) and inflammatory bowel diseases. However, visualization of the clinical workspace is limited to the distal tip camera. Thus, bleeding behind the tip can go undetected and pose safety risks to patients. This work presents a wireless soft optical blood sensor as an “add‐on” device for the colonoscope, utilizing light attenuation for bleeding detection. The multiple‐sleeve sensor design is made of microelectronic components with a flexible printed circuit board (PCB). The blood detection capability is characterized by various blood analog concentrations. The data rate, latency, and signal‐to‐noise ratio (SNR) are investigated to ensure the quality of real‐time bleeding detection and effective data transmission. The wireless blood sensor successfully performs in ex vivo trials without breaking down throughout 40 trials in three novices and one expert user. This works presents an “add‐on” wireless soft optical blood sensor for the traditional colonoscopy. It can reliably detect bleeding behind the colonoscope's distal tip camera via optical absorption, without optical fibers, and wirelessly transmit data to the processor. The sensor is successfully validated through colonoscopies in ex vivo bovine colon model.

Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).

Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).

Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. As a result, data usage is inefficient and analysis time is extensive. In addition to post-acquisition analysis, new APIs from major microscope manufacturers enable real-time, ML-based analytics for automated decision-making and ML-agent-controlled microscope operation. Yet, a gap remains between the ML and microscopy communities, limiting the impact of these methods on physics, materials discovery, and optimization. Hackathons help bridge this divide by fostering collaboration between ML researchers and microscopy experts. They encourage the development of novel solutions that apply ML to microscopy, while preparing a future workforce for instrumentation, materials science, and applied ML. This hackathon produced benchmark datasets and digital twins of microscopes to support community growth and standardized workflows. All related code is available at GitHub: https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1