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Near‐Infrared‐II (NIR‐II) spans wavelengths between 1,000 to 1,700 nanometers, featuring deep tissue penetration and reduced tissue scattering and absorption characteristics, providing robust support for cancer treatment and tumor imaging research. This review explores the utilization of activatable NIR‐II photodiagnosis and phototherapy based on tumor microenvironments (e. g. reactive oxygen species, pH, glutathione, hypoxia) and external stimulation (e. g. laser, ultrasound, photothermal) for precise tumor treatment and imaging. Special emphasis is placed on the advancements and advantages of activatable NIR‐II nanomedicines in novel therapeutic modalities like photodynamic therapy, photothermal therapy, and photoacoustic imaging. This encompasses achieving deep tumor penetration, real‐time monitoring of the treatment process, and obtaining high‐resolution, high signal‐to‐noise ratio images even at low material concentrations. Lastly, from a clinical perspective, the challenges faced by activatable NIR‐II phototherapy are discussed, alongside potential strategies to overcome these hurdles. [ABSTRACT FROM AUTHOR]

Background: Tumor fibrosis plays an important role in chemotherapy resistance in pancreatic ductal adenocarcinoma (PDAC), however there remains a contradiction in the prognostic value of fibrosis. We aimed to investigate the relationship between tumor fibrosis and survival in patients with PDAC, classify patients into high- and low-fibrosis groups, and develop and validate a CT-based radiomics model to non-invasively predict fibrosis before treatment., Materials and Methods: This retrospective, bicentric study included 295 patients with PDAC without any treatments before surgery. Tumor fibrosis was assessed using the collagen fraction (CF). Cox regression analysis was used to evaluate the associations of CF with overall survival (OS) and disease-free survival (DFS). Receiver operating characteristic (ROC) analyses were used to determine the rounded threshold of CF. An integrated model (IM) was developed by incorporating selected radiomic features and clinical-radiological characteristics. The predictive performance was validated in the test cohort (Center 2)., Results: The CFs were 38.22±6.89% and 38.44±8.66% in center 1 (131 patients, 83 males) and center 2 (164 patients, 100 males), respectively (P=0.814). Multivariable Cox regression revealed that CF was an independent risk factor in the OS and DFS analyses at both centers. ROCs revealed that 40% was the rounded cut-off value of CF. IM predicted CF with areas under the curves (AUCs) of 0.825 (95% confidence interval [CI], 0.749-0.886) and 0.745 (95% CI, 0.671-0.810) in the training and test cohorts, respectively. Decision curve analyses revealed that IM outperformed radiomics model and clinical-radiological model for CF prediction in both cohorts., Conclusions: Tumor fibrosis was an independent risk factor for survival of patients with PDAC, and a rounded cut-off value of 40% provided a good differentiation of patient prognosis. The model combining CT-based radiomics and clinical-radiological features can satisfactorily predict survival-grade fibrosis in patients with PDAC., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Most primary bone tumors are often found in the bone around the knee joint. However, the detection of primary bone tumors on radiographs can be challenging for the inexperienced or junior radiologist. This study aimed to develop a deep learning (DL) model for the detection of primary bone tumors around the knee joint on radiographs., Methods: From four tertiary referral centers, we recruited 687 patients diagnosed with bone tumors (including osteosarcoma, chondrosarcoma, giant cell tumor of bone, bone cyst, enchondroma, fibrous dysplasia, etc.; 417 males, 270 females; mean age 22.8±13.2 years) by postoperative pathology or clinical imaging/follow-up, and 1,988 participants with normal bone radiographs (1,152 males, 836 females; mean age 27.9±12.2 years). The dataset was split into a training set for model development, an internal independent and an external test set for model validation. The trained model located bone tumor lesions and then detected tumor patients. Receiver operating characteristic curves and Cohen's kappa coefficient were used for evaluating detection performance. We compared the model's detection performance with that of two junior radiologists in the internal test set using permutation tests., Results: The DL model correctly localized 94.5% and 92.9% bone tumors on radiographs in the internal and external test set, respectively. An accuracy of 0.964/0.920, and an area under the receiver operating characteristic curve (AUC) of 0.981/0.990 in DL detection of bone tumor patients were for the internal and external test set, respectively. Cohen's kappa coefficient of the model in the internal test set was significantly higher than that of the two junior radiologists with 4 and 3 years of experience in musculoskeletal radiology (Model vs. Reader A, 0.927 vs. 0.777, P<0.001; Model vs. Reader B, 0.927 vs. 0.841, P=0.033)., Conclusions: The DL model achieved good performance in detecting primary bone tumors around the knee joint. This model had better performance than those of junior radiologists, indicating the potential for the detection of bone tumors on radiographs., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1743/coif). All authors report the funding from the Natural Science Foundation of Guangdong Province, China (No. 2022A1515011593) and the Medical Research Foundation of Guangdong Province, China (No. A2021010). The authors have no other conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Near-Infrared-II (NIR-II) spans wavelengths between 1,000 to 1,700 nanometers, featuring deep tissue penetration and reduced tissue scattering and absorption characteristics, providing robust support for cancer treatment and tumor imaging research. This review explores the utilization of activatable NIR-II photodiagnosis and phototherapy based on tumor microenvironments (e. g., reactive oxygen species, pH, glutathione, hypoxia) and external stimulation (e. g., laser, ultrasound, photothermal) for precise tumor treatment and imaging. Special emphasis is placed on the advancements and advantages of activatable NIR-II nanomedicines in novel therapeutic modalities like photodynamic therapy, photothermal therapy, and photoacoustic imaging. This encompasses achieving deep tumor penetration, real-time monitoring of the treatment process, and obtaining high-resolution, high signal-to-noise ratio images even at low material concentrations. Lastly, from a clinical perspective, the challenges faced by activatable NIR-II phototherapy are discussed, alongside potential strategies to overcome these hurdles., (© 2024 Wiley-VCH GmbH.)

Introduction: MicroRNAs (miRNAs) are a class of non-coding RNA molecules that play a crucial role in the regulation of diverse biological processes across various organisms. Despite not encoding proteins, miRNAs have been found to have significant implications in the onset and progression of complex human diseases. Methods: Conventional methods for miRNA functional enrichment analysis have certain limitations, and we proposed a novel method called MiRNA Set Enrichment Analysis based on Multi-source Heterogeneous Information Fusion (MHIF-MSEA). Three miRNA similarity networks (miRSN-DA, miRSN-GOA, and miRSN-PPI) were constructed in MHIF-MSEA. These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. These miRNA similarity networks were fused into a single similarity network with the averaging method. This fused network served as the input for the random walk with restart algorithm, which expanded the original miRNA list. Finally, MHIF-MSEA performed enrichment analysis on the expanded list. Results and Discussion: To determine the optimal network fusion approach, three case studies were introduced: colon cancer, breast cancer, and hepatocellular carcinoma. The experimental results revealed that the miRNA-miRNA association network constructed using miRSN-DA and miRSN-GOA exhibited superior performance as the input network. Furthermore, the MHIF-MSEA model performed enrichment analysis on differentially expressed miRNAs in breast cancer and hepatocellular carcinoma. The achieved p -values were 2.17e (-75) and 1.50e (-77) , and the hit rates improved by 39.01% and 44.68% compared to traditional enrichment analysis methods, respectively. These results confirm that the MHIF-MSEA method enhances the identification of enriched miRNA sets by leveraging multiple sources of heterogeneous information, leading to improved insights into the functional implications of miRNAs in complex diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Ma, Lin, Zhao, Li and Huang.)

Objective: To explore the efficacy of Danshen injection combined with calcitriol and calcium/Vitamin-D in the treatment of osteoporotic fractures., Methods: This was a case-control study. We retrospectively reviewed clinical data of 91 patients with osteoporotic fractures who received treatment in Rui'an People's Hospital from February 2021 to July 2022. The data were divided into a control group with 44 records of patients who received treatment with calcitriol and calcium/Vitamin-D, and a study group with 47 patients who received Danshen injection combined with calcitriol and calcium/Vitamin-D. The control group individuals were coordinated with the patients in terms of their age and gender. Treatment effects, inflammatory response levels, and bone metabolic status levels were comparable between the two groups before and after the treatment., Results: The total efficacy of the treatment in the study group was better than that in the control group ( P <0.05). After the treatment, levels of serum inflammatory factors in both groups decreased compared to those before the treatment, and the study group displayed lower levels than the control group ( P <0.05). After the treatment, the bone metabolism status of both groups improved, and the improvement effect of the study group was better ( P <0.05). The incidences of adverse reactions were similar in both groups ( P >0.05)., Conclusions: Danshen injection combined with calcitriol and calcium/Vitamin-D for the treatment of osteoporotic fractures can effectively reduce inflammation, regulate bone metabolism, and improve fracture treatment efficacy with a favorable safety profile., (Copyright: © Pakistan Journal of Medical Sciences.)