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As the burgeoning field of Artificial Intelligence (AI) continues to permeate the fabric of healthcare, particularly in the realms of patient surveillance and telemedicine, a transformative era beckons. This manuscript endeavors to unravel the intricacies of recent AI advancements and their profound implications for reconceptualizing the delivery of medical care. Through the introduction of innovative instruments such as virtual assistant chatbots, wearable monitoring devices, predictive analytic models, personalized treatment regimens, and automated appointment systems, AI is not only amplifying the quality of care but also empowering patients and fostering a more interactive dynamic between the patient and the healthcare provider. Yet, this progressive infiltration of AI into the healthcare sphere grapples with a plethora of challenges hitherto unseen. The exigent issues of data security and privacy, the specter of algorithmic bias, the requisite adaptability of regulatory frameworks, and the matter of patient acceptance and trust in AI solutions demand immediate and thoughtful resolution .The importance of establishing stringent and far-reaching policies, ensuring technological impartiality, and cultivating patient confidence is paramount to ensure that AI-driven enhancements in healthcare service provision remain both ethically sound and efficient. In conclusion, we advocate for an expansion of research efforts aimed at navigating the ethical complexities inherent to a technology-evolving landscape, catalyzing policy innovation, and devising AI applications that are not only clinically effective but also earn the trust of the patient populace. By melding expertise across disciplines, we stand at the threshold of an era wherein AI's role in healthcare is both ethically unimpeachable and conducive to elevating the global health quotient.

Chimeric antigen receptor T (CAR-T) cell therapy achieved extraordinary achievements results in antitumor treatments, especially against hematological malignancies, where it leads to remarkable, long-term antineoplastic effects with higher target specificity. Nevertheless, some limitations persist in autologous CAR-T cell therapy, such as high costs, long manufacturing periods, and restricted cell sources. The development of a universal CAR-T (UCAR-T) cell therapy is an attractive breakthrough point that may overcome most of these drawbacks. Here, we review the progress and challenges in CAR-T cell therapy, especially focusing on comprehensive comparison in UCAR-T cell therapy to original CAR-T cell therapy. Furthermore, we summarize the developments and concerns about the safety and efficiency of UCAR-T cell therapy. Finally, we address other immune cells, which might be promising candidates as a complement for UCAR-T cells. Through a detailed overview, we describe the current landscape and explore the prospect of UCAR-T cell therapy.

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles 1 – 3 . Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context 4 . Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet 5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data 6 . With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision–language pretraining for pathology 7 , 8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling. Prov-GigaPath, a whole-slide pathology foundation model pretrained on a large dataset containing around 1.3 billion pathology images, attains state-of-the-art performance in cancer classification and pathomics tasks.

The management of pediatric severe asthma poses significant challenges for families. When faced with the choice between targeted biologics like omalizumab and widely used complementary alternative medicine (CAM), families navigate a complex decision-making process influenced by multiple factors. This review adopts a family-centered perspective to systematically analyze key factors influencing this trade-off: treatment goals (extending beyond clinical metrics to focus on quality of life), risk perception (shaped by subjective constructs and lacking direct evidence for comparative risk assessments), treatment burden (often overlooked hidden costs), and the current state of shared decision-making (SDM). Analysis reveals that family decision-making is a multidimensional construct shaped by four core elements: value systems, lived experiences, risk perception patterns, and tolerance for treatment burden. Notably, the significant gap in risk perception evidence leads to subjective risk assessments dominating decisions, particularly in CAM choices. Treatment burden, a critical hidden cost, is often marginalized in decisions, hindering effective SDM. Health equity further profoundly impacts choices. The conclusion emphasizes the need for clinical practice to shift toward family-centered care by addressing real-world needs, routinely evaluating treatment burden, optimizing risk communication, overcoming SDM barriers, and promoting health equity. Future research must fill evidence gaps in risk perception, develop SDM tools, and address culturally diverse family needs.

Machine learning (ML) has played a crucial role in advancing precision immunotherapy by integrating multi-omics data to identify biomarkers and predict therapeutic responses. However, a prevalent methodological flaw persists in immunological studies—an overreliance on correlation-based analysis while neglecting causal inference. Traditional ML models struggle to capture the intricate dynamics of immune interactions and often function as “black boxes.” A systematic review of 90 studies on immune checkpoint inhibitors revealed that despite employing ML or deep learning techniques, none incorporated causal inference. Similarly, all 36 retrospective studies modeling melanoma exhibited the same limitation. This “knowledge–practice gap” highlights a disconnect: although researchers acknowledge that correlation does not imply causation, causal inference is often omitted in practice. Recent advances in causal ML, like Targeted-BEHRT, CIMLA, and CURE, offer promising solutions. These models can distinguish genuine causal relationships from spurious correlations, integrate multimodal data—including imaging, genomics, and clinical records—and control for unmeasured confounders, thereby enhancing model interpretability and clinical applicability. Nevertheless, practical implementation still faces major challenges, including poor data quality, algorithmic opacity, methodological complexity, and interdisciplinary communication barriers. To bridge these gaps, future efforts must focus on advancing research in causal ML, developing platforms such as the Perturbation Cell Atlas and federated causal learning frameworks, and fostering interdisciplinary training programs. These efforts will be essential to translating causal ML from theoretical innovation to clinical reality in the next 5-10 years—representing not only a methodological upgrade, but also a paradigm shift in immunotherapy research and clinical decision-making.

Brassinolide (BR) is a sterol compound, which can regulate plant seed germination, flowering, senescence, tropism, photosynthesis, stress resistance, and is closely related to other signaling molecules. This study aimed to evaluate the ability of soaking with BR to regulate growth quality at rice seedling stage under salt stress. Results demonstrated that salt stress increases the contents of ROS, MDA, Na + and ABA, reduces the the SPAD value, net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), maximum fluorescence (Fm), variable fluorescence (Fv), the effective photochemical efficiency of PSII (Fv/Fo) and the maximum photochemical efficiency of PSII (Fv/Fm), reduces the biomass production and inhabits plant growth. All of these responses were effectively alleviated by BR soaking treatment. Soaking with BR could increase the activities of superoxide dismutase, peroxidase, catalase, ascorbate peroxidase, and the contents of ascorbic acid, glutathione as well as soluble protein and proline, while BR soaking treatment inhibited the accumulation of ROS and reduced the content of MDA. BR soaking significantly reduced the contents of Na + and increased the contents of K + and Ca 2+ , indicating that soaking with BR is beneficial to the excretion of Na + , the absorption of K + and Ca 2+ and the maintenance of ion balance in rice seedlings under salt stress. BR also maintained endogenous hormone balance by increasing the contents of indoleacetic acid (IAA), zeatin (ZT), salicylic acid (SA), and decreasing the ABA content. Soaking with BR significantly increased the SPAD value, Pn and Tr and enhanced the Fm, Fv/Fm and Fv/Fo of rice seedlings under NaCl stress, protected the photosythetic system of plants, and improved their biomass. It is suggested that BR was beneficial to protect membrane lipid peroxidation, the modulation of antioxidant defense systems, ion balance and endogenous hormonal balance with imposition to salt stress.

The current polymer-exchange membrane fuel cell technology cannot directly use biomass as fuel. Here we present a solar-induced hybrid fuel cell that is directly powered with natural polymeric biomasses, such as starch, cellulose, lignin, and even switchgrass and wood powders. The fuel cell uses polyoxometalates as the photocatalyst and charge carrier to generate electricity at low temperature. This solar-induced hybrid fuel cell combines some features of solar cells, fuel cells and redox flow batteries. The power density of the solar-induced hybrid fuel cell powered by cellulose reaches 0.72 mW cm −2 , which is almost 100 times higher than cellulose-based microbial fuel cells and is close to that of the best microbial fuel cells reported in literature. Unlike most cell technologies that are sensitive to impurities, the cell reported in this study is inert to most organic and inorganic contaminants present in the fuels. The direct conversion of biomass to electricity is an important process. Here, the authors use polyoxometallates as both photocatalyst and charge carrier to generate electricity in a solar-powered hybrid fuel cell that can consume natural biomass, such as cellulose or wood powders, at low temperature.

Two major treatment strategies employed in non-small cell lung cancer, NSCLC, are tyrosine kinase inhibitors, TKIs, and immune checkpoint inhibitors, ICIs. The choice of strategy is based on heterogeneous biomarkers that can dynamically change during therapy. Thus, there is a compelling need to identify comprehensive biomarkers that can be used longitudinally to help guide therapy choice. Herein, we report a 18 F-FDG-PET/CT-based deep learning model, which demonstrates high accuracy in EGFR mutation status prediction across patient cohorts from different institutions. A deep learning score (EGFR-DLS) was significantly and positively associated with longer progression free survival (PFS) in patients treated with EGFR-TKIs, while EGFR-DLS is significantly and negatively associated with higher durable clinical benefit, reduced hyperprogression, and longer PFS among patients treated with ICIs. Thus, the EGFR-DLS provides a non-invasive method for precise quantification of EGFR mutation status in NSCLC patients, which is promising to identify NSCLC patients sensitive to EGFR-TKI or ICI-treatments. EGFR mutations are common in non-small cell lung cancer and patients with these mutations are treated with tyrosine kinase inhibitors. Here, the authors show that EGFR mutation status can be predicted from 18 F-FDG-PET/CT images, which may enable the stratification of patients for treatment.

Programmed death-1 (PD-1) and its ligand PD-L1 inhibitors have become pivotal agents in cancer immunotherapy, demonstrating significant efficacy across multiple malignancies. However, beyond regulating T cell activation, the PD-1/PD-L1 axis also exerts complex and critical effects on bone metabolism. Notably, both clinical observations and mechanistic studies have revealed a paradox: on one hand, PD-1/PD-L1 blockade appears to confer bone-protective benefits; on the other hand, it has been associated with bone-related adverse events (AEs) in up to 69% of patients, including pathological fractures and vertebral compression fractures. This review comprehensively explores the bidirectional regulatory effects of the PD-1/PD-L1 pathway on bone metabolism and investigates the underlying mechanisms contributing to these contradictory findings. The discrepancies may be attributed to a combination of clinical variables, microenvironmental conditions, cell-specific responses, and intricate interactions among multiple signaling pathways, including the Wnt/β-Catenin pathway and the PD-L1–PKM2 axis. We further examine the pathophysiological basis of osteoporosis and fragility fractures occurring during PD-1/PD-L1 inhibitor therapy, and argue for their recognition as a subclass of immune-related adverse events (irAEs). Finally, we propose a framework for bone health surveillance and stratified prevention strategies aimed at preserving antitumor efficacy while improving skeletal health and quality of life—offering novel insights into osteoporosis prevention and management in the context of immune checkpoint inhibition.

Habits such as smoking and alcohol drinking and existing esophageal malfunction are considered the main risk factors for esophageal carcinogenesis. Caustic ingestion of acidic or alkaline agents or strong irritants can induce severe esophageal corrosive injury and increase esophageal cancer risk. We studied the relationship between esophageal carcinoma and acute detergent or pesticide poisoning by using nationwide health insurance data. Methodology/Principle findings : We compared a pesticide/detergent intoxication cohort (N = 21,840) and an age- and gender-matched control cohort (N = 21,840) identified from the National Health Insurance Research Database between 2000 and 2011. We used the multivariable Cox proportional model to determine esophageal carcinoma risk. The overall incidence density of esophageal cancer was 1.66 per 10,000 person-years in the comparison cohort and 4.36 per 10,000 person-years in the pesticide/detergent intoxication cohort. The corresponding adjusted hazard ratio (HR) for esophageal cancer was 2.33 (95% confidence interval [CI] = 1.41–3.86) in the pesticide/detergent intoxication cohort compared with the control cohort. Patients with corrosive and detergent intoxication did not have a higher risk of esophageal cancer (adjusted HR = 0.98, 95% CI = 0.29–3.33) than those without pesticide/detergent intoxication. However, patients with pesticide intoxication had a significantly higher risk of esophageal cancer (adjusted HR = 2.52, 95% CI = 1.52–4.18) than those without pesticide/detergent intoxication. Conclusion : In the present study, after adjusting for conventional risk factors, we observed that pesticide intoxication could exert substantial effects through increased esophageal cancer risk. However, patients with detergent intoxication may not have an increased risk of esophageal cancer.