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Cultural heritage (CH) relics are irreplaceable records of human civilization, encompassing diverse historical, technological, and artistic achievements. Extracting their structural and compositional information without affecting their physical integrity is a critical challenge. This review summarizes recent advances in non-destructive techniques (NDTs) for CH analysis and emphasizes the balance between the depth of analysis and conservation ethics. Techniques are broadly categorized into spectrum-based, X-ray-based, and digital-based methods. Spectroscopic techniques such as Fourier transform infrared (FTIR), Raman, and nuclear magnetic resonance (NMR) spectroscopy provide molecular-level insights into organic and inorganic components, often requiring minimal or no sampling. X-ray-based techniques, including conventional and spatially resolved XRD/XRF and total reflection XRF (TRXRF), provide powerful means for crystal and elemental analysis, including in situ pigment identification and trace material analysis. Digital-based methods include high-resolution imaging, three-dimensional modeling, data fusion, and AI-driven diagnosis to achieve the non-invasive visualization, monitoring, and virtual restoration of CH assets. This review highlights a methodology shift from traditional molecular-level detection to data-centric and AI-assisted diagnosis, reflecting the paradigm shift in heritage science.

Purpose This paper aims to build on the latest advances in cognitive computing techniques to systematically illustrate how unstructured data from users can offer significant value for co-innovation. Design/methodology/approach The paper adopts a general overview approach to understand how unstructured data from users can be analyzed with cognitive computing techniques for innovation. The paper links the computerized techniques with marketing innovation problems with an integrated framework using dynamic capabilities and complexity theory. Findings The paper identifies a suite of methodologies for facilitating company co-innovation via engaging with customers and external data with cognitive computing technologies. It helps to expand marketing researchers and practitioners’ understanding of using unstructured data. Research limitations/implications This paper provides a conceptual framework that divides co-innovation process into three stages, ideas generation, ideas integration and ideas evaluation, and maps cognitive computing methodologies and technologies to each stage. This paper makes the theoretical contributions by developing propositions from both customer and firm perspectives. Practical implications This paper can be used for companies to engage consumers and external data for co-innovation activities by strategically select appropriate cognitive computing techniques to analyze unstructured data for better insights. Originality/value Given the lack of systematic discussion regarding what is possible from using cognitive computing to analyze unstructured data for co-innovation. This paper makes first attempt to summarize how unstructured data can be analyzed with cognitive computing techniques. This paper also integrates complexity theory to the framework from a novel perspective.

Melatonin has been shown to diminish edema in rats. Melatonin can be used to treat spinal cord injury. This study presumed that melatonin could relieve spinal cord edema and examined how it might act. Our experiments found that melatonin （100 mg/kg, i.p.） could reduce the water content of the spinal cord, and suppress the expression of aquaporin-4 and glial fibrillary acidic protein after spinal cord injury. This suggests that the mechanism by which melatonin alleviates the damage to the spinal cord by edema might be related to the expression of aquaporin-4 and glial fibrillary acidic protein.

Interest in machine-learning applications within medicine has been growing, but few studies have progressed to deployment in patient care. We present a framework, context and ultimately guidelines for accelerating the translation of machine-learning-based interventions in health care. To be successful, translation will require a team of engaged stakeholders and a systematic process from beginning (problem formulation) to end (widespread deployment). In this Perspective, the authors present a framework, context and guidelines for accelerating the translation of machine-learning-based interventions in health care.

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.

Background To compare proprioception recovery after anterior cruciate ligament reconstruction (ACLR) with a hamstring tendon autograft versus the artificial Ligament Advanced Reinforcement System (LARS). Material and methods Forty patients (9 females, 31 males) with anterior cruciate ligament (ACL) rupture were enrolled in this prospective study. Patients were randomized to two groups, 1) ACLR using a hamstring tendon autograft ( n  = 20) or 2) ACLR using artificial LARS ( n  = 20). Proprioception was assessed with knee joint position sense (JPS) passive-passive test at 45° and 75° flexions, with the contralateral healthy knee as a control baseline to calculate the JPS error. Knee JPS absolute error was used as the main outcome variable and defined as the absolute difference between the reproduction and target angles. Results JPS error in both groups at 3 months after ACLR was significantly higher than that at 12 months. However, no significant difference in JPS error was detected between the LARS and autograft groups at either 3 or 12 months after ACLR. Analyzing JPS data by grouping patients according to whether ACLR was performed more or less than 1 year following injury regardless of graft type showed a statistically significant difference between the groups at 3 months, but not at 12 months, after ACLR. Patients receiving the graft within 1 year of injury had a lower JPS error than those receiving the graft more than 1 year after injury at 3 months. No complications were associated with either ACLR method. Conclusion ACLR with a hamstring tendon autograft or LARS artificial graft is similarly safe and effective for recovering knee proprioception.

Background The prognosis of osteosarcoma has improved little over the past few decades, with radioresistance being a contributing factor. Effective radiosensitizing targets and novel mechanisms for treating osteosarcoma are urgently needed. Research on the impact of regulating differentiation levels on the radiosensitivity of malignant tumors is limited. This study aimed to explore the efficacy of ITGB3 as a novel radiosensitizing target in osteosarcoma and to explore whether the modulation of osteogenic differentiation plays a role in mediating the radiosensitizing effect. Methods RNA sequencing was utilized to screen for potential targets that affect the radiosensitivity of osteosarcoma. In vitro assays examining cell viability, apoptosis, proliferation, migration, and invasion were conducted to verify the radiosensitizing effect of ITGB3-knockdown (KD). Furthermore, in vivo validation was performed by constructing mouse models with subcutaneous and orthotopic tibial tumors. Rescue experiments involving siRNAs and molecular inhibitors were performed to explore and validate the mechanisms through which ITGB3-KD exerts a radiosensitizing effect in vitro and in vivo. Additionally, osteogenic differentiation cultures of osteosarcoma cells were conducted as auxiliary validation for the radiosensitizing mechanism. Results ITGB3-KD had a radiosensitizing effect on osteosarcoma in vitro by inhibiting cell viability, proliferation, migration, and invasion and promoting apoptosis. ITGB3-KD radiosensitized osteosarcoma in vivo in subcutaneous and orthotopic tibial tumor models. ITGB3-KD upregulated the JNK/c-JUN pathway, and rescue experiments with a JNK inhibitor revealed that the activation of this pathway was crucial for the upregulation of osteogenic markers such as RUNX2, OCN, and OPN, as well as for promoting apoptotic pathways. siRNA-based rescue experiments indicated that the upregulation of RUNX2 mediated the proapoptotic radiosensitizing effects of ITGB3-KD. Culture in osteogenic differentiation medium promoted osteosarcoma radiosensitization by enhancing the osteogenic differentiation status, working synergistically with ITGB3-KD. Conclusions Our findings indicate that ITGB3-KD enhances radiosensitivity in osteosarcoma by promoting osteogenic differentiation and apoptosis through activation of the JNK/c-JUN/RUNX2 pathway, identifying ITGB3 as a candidate therapeutic target and implicating JNK/c-JUN/RUNX2 signaling as a modulatory axis for improving the response to radiation of osteosarcoma.

Background Limb salvage reconstruction for pelvic tumors, especially periacetabular tumors, is challenging. We combined the use of dual mobility bearing and 3D-printed hemipelvic prosthesis to improve function and reduce the probability of complications after hemi-pelvic resection in patients with primary acetabular malignancy. The purpose of this study was to evaluate the efficacy and safety of this combination. Methods Between October 2011 and May 2021, 11 patients with malignancies involving the acetabulum received hemipelvic replacement with a 3D-printed prosthesis and dual mobility bearing. Follow‐up of postoperative survival, complications, and Musculoskeletal Tumor Society 93 (MSTS-93) lower limb functional scores were carried out. A finite element model of the postoperative pelvis was developed and input into the finite element analysis software. The Von Mises equivalent stress formula was used to analyze the stress distribution of each part of the pelvis under one gait cycle and the stress distribution at different angles of the hip joint. Results By the last follow-up, 9 of the 11 patients (81.8%) were still alive, and 2 patients had local tumor recurrence. The complications including 1 deep infection and 1 dislocation of the artificial joint. Excluding 1 amputation patient, the average score of the remaining 8 patients at the last follow-up was 21.4/30 (71.3%) on the MSTS-93. In the reconstructed pelvis, stress distributions were concentrated on the junction between hemipelvic prosthesis and screw and iliac bone on the resected side, and between femoral prosthesis stem and femoral bulb, while the stress of polyethylene lining was small. Before impact, the polyethylene lining will rotate at a small angle, about 3°. The inner stress of polyethylene liner is greater than the outer stress in all conditions. The polyethylene liner has no tendency to slide out. Conclusion Pelvic tumor resection and reconstruction using 3D-printed hemipelvic prosthesis combined with dual mobility bearing was an effective treatment for pelvic tumors. Our patients achieved good early postoperative efficacy and functional recovery. The dual mobility bearing is beneficial to prevent dislocation, and the mechanical distribution and wear of the prosthesis are acceptable.

Bone marrow mesenchymal stem cell (BMSC) is previously reported to present a certain effect on treating spinal cord injury (SCI), while the underlying mechanism is largely uncovered. Therefore, the current study aimed to investigate the involvement of exosome-delivered circRNA profile in the BMSC’s effect on pyroptosis for SCI treatment. H 2 O 2 treated rat primary neurons were cultured with normal medium, BMSC, BMSC plus GW4869, and BMSC-derived exosome, respectively, then inflammasome-related pyroptosis markers, and circRNA profiles were detected. Subsequently, circ_003564-knockdown BMSC exosome was transfected into H 2 O 2 treated rat primary neurons and NGF-stimulated PC-12 cells. Furthermore, in vivo validation was conducted. BMSC and BMSC-derived exosome both decreased inflammasome-related pyroptosis markers including cleaved caspase-1, GSDMD, NLRP3, IL-1β, and IL-18 in H 2 O 2 -treated neurons, while exosome-free BMSC (BMSC plus GW4869) did not obviously reduce these factors. Microarray assay revealed that BMSC (vs. exosome-free BMSC) and BMSC-derived exosome (vs. normal medium) greatly regulated circRNA profiles, which were enriched in neuroinflammation pathways (such as neurotrophin, apoptosis, and TNF). Among three functional candidate circRNAs (circ_015525, circ_008876, and circ_003564), circ_003564 was most effective to regulate inflammasome-related pyroptosis. Interestingly, circ_003564-knockdown BMSC exosome showed higher expression of inflammasome-related pyroptosis markers compared to negative-control-knockdown BMSC exosome in H 2 O 2 treated primary neurons/NGF-stimulated PC-12 cells. In vivo, BMSC exosome improved the function recovery and decreased tissue injury and inflammasome-related pyroptosis in SCI rats, whose effect was attenuated by circ_003564 knockdown transfection. BMSC exosome attenuates inflammasome-related pyroptosis via delivering circ_003564, contributing to its treatment efficacy for SCI.

Background Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed. Main body Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent “black-box” nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools. Conclusions AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.