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A series of novel zinc-salophen (salophen = N, N′-phenylenebis(salicylimine)) complexes (Zn-1–4) with electron donor–acceptor (D–A) structure were synthesized and characterized using a triphenylamine structure as the electron donor. Zn-salophen complexes with the same substituent sites have been reported to exhibit significant CT properties. The design of the D–A structure and the increase in the number of benzene rings to increase the length of bridging groups have led to a reduction in the energy difference between charge separation singlet and triplet states, resulting in the production of reactive oxygen species (ROS) under light irradiation. The ability has been enhanced (in terms of the production of singlet oxygen (1O2), compared with Zn-salophen, Zn-4 is 1.58 times higher). This method has been reported to enhance the intersystem crossing process of compounds, thereby enabling them to reach a triple excited state, but the generation of ROS has not been studied. Although the enhancement is not very significant, it has expanded the medical application prospects of these types of complexes and has provided a new strategy to enhance the production of ROS.

In order to enhance the service life of refractories for melting and casting of titanium alloy, preparation of yttrium oxide coatings on the refractories surface is an effective solution. The improvement of thermal shock stability of the coating is beneficial to its spalling resistance during the high-temperature service, and the available slurry with good performance is the prerequisite. The effects of the varieties and contents of dispersant (sodium tripolyphosphate, sodium hexametaphosphate, sodium pyrophosphate, citric acid and polycarboxylate ammonium salt) and binder (aluminum dihydrogen phosphate, zirconium acetate and yttria sol) on the stability, rheological and thixotropic properties of yttrium oxide water-based slurry were investigated, and the effects of slurry composition design on the thermal shock resistance of the coating were focused. The results showed that the introduction of polycarboxylate ammonium salt as dispersant significantly improved the stability of the slurry, and the varieties of the binders had significant effects on the dispersibility of the slurry. When aluminum dihydrogen phosphate and polycarboxylate ammonium salt were used as binder and dispersant, respectively, the slurry had better stability, suitable rheological and thixotropic properties. By using aluminum dihydrogen phosphate as binder, the coating had good thermal shock resistance and no obvious cracks were observed for the coating after thermal shock, which was attributed to the yttrium aluminate binding phase generated in the coating. An effective method was provided for preparing yttrium oxide coatings with enhanced thermal shock resistance applied in the process of titanium alloy melting and casting.

DNA data storage is a rapidly developing technology with great potential due to its high density, long-term durability, and low maintenance cost. The major technical challenges include various errors, such as strand breaks, rearrangements, and indels that frequently arise during DNA synthesis, amplification, sequencing, and preservation. In this study, a de novo strand assembly algorithm (DBGPS) is developed using de Bruijn graph and greedy path search to meet these challenges. DBGPS shows substantial advantages in handling DNA breaks, rearrangements, and indels. The robustness of DBGPS is demonstrated by accelerated aging, multiple independent data retrievals, deep error-prone PCR, and large-scale simulations. Remarkably, 6.8 MB of data is accurately recovered from a severely corrupted sample that has been treated at 70 °C for 70 days. With DBGPS, we are able to achieve a logical density of 1.30 bits/cycle and a physical density of 295 PB/g. DNA data storage is a rapidly developing technology with great potential due to its high density, long-term durability, and low maintenance cost. Here the authors present a strand assembly algorithm (DBGPS) using de Bruijn graph and greedy path search.

Objective To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 × 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

Objective To develop a convolutional neural network (CNN) model for the automatic detection and classification of rib fractures in actual clinical practice based on cross-modal data (clinical information and CT images). Materials In this retrospective study, CT images and clinical information (age, sex and medical history) from 1020 participants were collected and divided into a single-centre training set ( n  = 760; age: 55.8 ± 13.4 years; men: 500), a single-centre testing set ( n  = 134; age: 53.1 ± 14.3 years; men: 90), and two independent multicentre testing sets from two different hospitals ( n  = 62, age: 57.97 ± 11.88, men: 41; n  = 64, age: 57.40 ± 13.36, men: 35). A Faster Region–based CNN (Faster R-CNN) model was applied to integrate CT images and clinical information. Then, a result merging technique was used to convert 2D inferences into 3D lesion results. The diagnostic performance was assessed on the basis of the receiver operating characteristic (ROC) curve, free-response ROC (fROC) curve, precision, recall (sensitivity), F1-score, and diagnosis time. The classification performance was evaluated in terms of the area under the ROC curve (AUC), sensitivity, and specificity. Results The CNN model showed improved performance on fresh, healing, and old fractures and yielded good classification performance for all three categories when both clinical information and CT images were used compared to the use of CT images alone. Compared with experienced radiologists, the CNN model achieved higher sensitivity (mean sensitivity: 0.95 > 0.77, 0.89 > 0.61 and 0.80 > 0.55), comparable precision (mean precision: 0.91 > 0.87, 0.84 > 0.77, and 0.95 > 0.70), and a shorter diagnosis time (average reduction of 126.15 s). Conclusions A CNN model combining CT images and clinical information can automatically detect and classify rib fractures with good performance and feasibility in actual clinical practice. Key Points • The developed convolutional neural network (CNN) performed better in fresh, healing, and old fractures and yielded a good classification performance in three categories, if both (clinical information and CT images) were used compared to CT images alone. • The CNN model had a higher sensitivity and matched precision in three categories than experienced radiologists with a shorter diagnosis time in actual clinical practice.

This study aims to assess the prevalence of geriatric syndromes and identify factors associated with multiple geriatric syndromes in community-dwelling older adults in China. We utilized a convenience sampling method to recruit older adults and from one rural and one urban community in Chengdu, China, from October 2022 to March 2023. A total of 706 older adults aged 60 years or older were included. Ten geriatric syndromes were investigated including two mental disorders: depressive symptoms, cognitive impairment; and eight somatic disorders: pain, falls, sleep disturbance, constipation, polypharmacy, multimorbidity, malnutrition and frailty. Multiple geriatric syndromes were defined as an individual having two or more geriatric syndromes. The data obtained were analysed using descriptive statistics. The independent risk factors for multiple geriatric syndromes were assessed using a logistic regression model. This study found that 90.5% of the participants had at least one geriatric syndrome, with 72.8% experiencing multiple geriatric syndromes. The top four geriatric syndromes in our study were polypharmacy (58.5%), malnutrition/at risk of malnutrition (43.1%), multimorbidity (42.1%), and frailty/prefrailty (34.3%). Of the older adults, 368(52.1%) had only somatic disorders, 18(2.5%) had only mental disorders and 253 (35.8%) had somatic-mental disorders. According to the logistic regression analysis, residence, age, marriage, BMI, and self-related health were significantly associated with multiple geriatric syndromes among older adults. This study highlights that multiple geriatric syndromes are prevalent among community-dwelling older adults in China, and underscores the significance of certain demographic factors in their occurrence. Future longitudinal studies are needed to establish the temporal relationship between multiple geriatric syndromes and these demographic factors, as well as to explore causal relationships and effective prevention strategies for geriatric syndrome.

Gallbladder cancer (GBC) is the most common malignant tumor of the biliary system, with poor response to current treatments. Abnormal alternative splicing has been associated with the development of a variety of tumors. Combining the GEO database and GBC mRNA‐seq analysis, it is found high expression of the splicing factor polypyrimidine region‐ binding protein 3 (PTBP3) in GBC. Multi‐omics analysis revealed that PTBP3 promoted exon skipping of interleukin‐18 (IL‐18), resulting in the expression of ΔIL‐18, an isoform specifically expressed in tumors. That ΔIL‐18 promotes GBC immune escape by down‐regulating FBXO38 transcription levels in CD8+T cells to reduce PD‐1 ubiquitin‐mediated degradation is revealed. Using a HuPBMC mouse model, the role of PTBP3 and ΔIL‐18 in promoting GBC growth is confirmed, and showed that an antisense oligonucleotide that blocked ΔIL‐18 production displayed anti‐tumor activity. Furthermore, that the H3K36me3 promotes exon skipping of IL‐18 by recruiting PTBP3 via MRG15 is demonstrated, thereby coupling the processes of IL‐18 transcription and alternative splicing. Interestingly, it is also found that the H3K36 methyltransferase SETD2 binds to hnRNPL, thereby interfering with PTBP3 binding to IL‐18 pre‐mRNA. Overall, this study provides new insights into how aberrant alternative splicing mechanisms affect immune escape, and provides potential new perspectives for improving GBC immunotherapy. PTBP3 mediated IL‐18 exon skipping to promote immune escape from gallbladder cancer. Furthermore, H3K36me3 regulated IL‐18 exon skipping by recruiting PTBP3 via MRG15 and coupled IL‐18 transcription and alternative splicing. Mechanistically, ΔIL‐18 decreased FBXO38 transcription levels in CD8+T cells to reduce FBXO38‐mediated degradation of PD‐1. During splicing, the hnRNPL bound to H3K36 methyltransferases SETD2 and this manner interfered the binding of PTBP3 to the IL‐18 pre‐mRNA.