Malignant tumors remain one of the most critical global public threats to human health. The early diagnosis and precise therapeutic interventions are pivotal for improving patient survival rates and prognosis. Gold nanoclusters (Au NCs), distinguished by their ultra-small size (<3 nm), tunable optical properties, and exceptional biocompatibility, have emerged as transformative agents in precision oncology. This comprehensive review systematically summarizes the multifaceted applications of Au NCs in malignant tumor treatment. We discuss their roles as follows. (1) Intelligent delivery vehicles for targeted chemotherapy and controlled release through surface functionalization. (2) Therapeutic agents for chemodynamic therapy (CDT). This capability stems from their intrinsic enzyme-like catalytic activity or potent thioredoxin reductase (TrxR) inhibitory function, which disrupts the intracellular redox homeostasis and effectively activates downstream apoptotic pathways.(3) Direct therapeutic agents are characterized by their energy conversion capabilities: they can either convert absorbed light into heat to directly kill cancer cells, or transfer that photon energy to surrounding oxygen molecules to generate cytotoxic reactive oxygen species (ROS), leading to cell apoptosis or necrosis. (4) Potent radiosensitizers that enhance radiotherapy efficacy by enhancing localized radiation dose and promoting ROS generation. This review systematically summarizes the recent advances in Au NCs as intelligent delivery systems, direct chemotherapeutic agents, phototherapeutic agents, and efficient radiosensitizers in tumor treatment, elucidating how Au NCs overcome traditional therapeutic limitations through synergistic strategy. It establishes a robust theoretical foundation for next-generation nanotheranostic platforms. However, the translation of laboratory findings into functional clinical technologies confronts three significant challenges. First, although researchers can synthesize atomically precise Au NCs, achieving large-scale production of batches with completely consistent structure, size, and surface chemistry remains extremely challenging. To effectively control the final synthetic product, a deep understanding of the characteristics and formation mechanisms of Au NCs is essential. The traditional “trial-and-error” experimental approach faces inherent limitations when dealing with vast combinations of variables, which is time-consuming, labor-intensive, and struggles with systematic exploration and reproducibility. Machine learning has emerged as a powerful tool to bridge fundamental research and clinical application, which can guide experiments in reverse by predicting synthesis success through data mining and multi-variable analysis. In the future, we anticipate to achieve precise prediction and on-demand design of Au NCs’ structure and properties. Secondly, a systematic framework for evaluating the in vivo pharmacokinetics and long-term toxicity of Au NCs is absent. To address this gap, it is crucial to develop advanced imaging methodologies and integrated theranostic platforms. Au NCs, serving as both a therapeutic core and a highly promising photoluminescent material, are key to constructing such platforms through integration with other agents. These multifunctional systems are designed to achieve optimal synergistic therapy by combining multiple treatment modalities. Finally, the investigation of Au NCs is still largely confined to preclinical cellular and animal studies. Progress necessitates comprehensive clinical research to rigorously assess their safety and efficacy across a range of human cancer models, thereby ensuring broad clinical applicability. In summary, Au NCs-based platforms hold immense promise for translation into clinical anticancer therapy.

Diabetic sarcopenia (DS) is a common but often ignored skeletal muscle complication in individuals with diabetes mellitus. It is characterized by progressive loss of skeletal muscle mass, reduced muscle strength, and impaired physical performance, which may further increase the risk of falls, disabilities, metabolic disorders, and adverse clinical outcomes. Traditionally, DS has been attributed mainly to hyperglycemia, insulin resistance, aging-related muscle decline, and chronic complications of diabetes. However, increasing evidence suggests that lipid metabolic disturbance and pathological lipid metabolic reprogramming are not merely secondary consequences of diabetes, but may actively participate in the initiation and progression of DS. Under diabetic conditions, impaired fatty acid uptake, transport, oxidation, and storage disrupt skeletal muscle metabolic homeostasis, leading to ectopic lipid deposition and accumulation of lipotoxic intermediates. These lipid-derived metabolites can aggravate insulin resistance, impair mitochondrial energy production, enhance oxidative stress, activate chronic low-grade inflammation, and disturb protein synthesis and degradation balance, thereby accelerating skeletal muscle atrophy and functional decline. Lipid metabolic dysregulation may also interact with multiple pathological processes involved in DS, including mitochondrial dysfunction, inflammatory signaling, oxidative damage, impaired autophagy, and gut microbiota imbalance. These mechanisms do not occur independently; instead, they form a complex bidirectional vicious cycle with diabetes-related metabolic disorders. Specifically, mitochondrial dysfunction reduces fatty acid oxidative capacity, which further promotes lipid accumulation and lipotoxicity. Inflammatory activation can impair insulin signaling and muscle protein metabolism, while lipid overload may in turn amplify inflammatory responses. Similarly, gut microbiota dysbiosis and altered microbial metabolites may influence systemic inflammation, lipid metabolism, and skeletal muscle homeostasis. Therefore, lipid metabolic reprogramming provides an important mechanistic perspective for understanding the progression of DS from metabolic disturbance to structural and functional muscle impairment. Exercise intervention is an effective and clinically feasible non-pharmacological strategy for the prevention and management of DS. Both aerobic exercise and resistance training have been shown to improve insulin sensitivity, enhance fatty acid oxidation, increase mitochondrial biogenesis, reduce ectopic lipid deposition, and attenuate lipotoxic metabolite accumulation. These adaptations not only improved glucose and lipid metabolism, but also increased the preservation of muscle mass, muscle strength, and physical function. Moreover, combined exercise strategies may provide complementary benefits by integrating the metabolic advantages of aerobic exercise with the anabolic and functional effects of resistance training. Based on analyses of publicly available datasets and literature evidences, this review systematically summarizes the role of lipid metabolic disorders in the pathogenesis of DS, with particular attention to the molecular mechanisms linking lipid dysregulation to insulin resistance, chronic inflammation, oxidative stress, mitochondrial dysfunction, and gut microbiota disturbance. Furthermore, this review discusses the potential mechanisms by which exercise intervention improves DS through the regulation of lipid metabolic reprogramming, and outlines exercise prescription strategies in terms of modality, intensity, frequency, and duration. Understanding the interaction between lipid metabolism and skeletal muscle dysfunction may provide new theoretical evidence for early identification, mechanistic research, and precision exercise therapy in DS. Overall, targeting pathological lipid metabolic reprogramming through exercise intervention represents a promising and clinically actionable approach for improving muscle health and prognosis in individuals with DS.

Objective To develop and validate a fusion model that integrates a multi-parametric magnetic resonance imaging deep learning algorithm with clinicopathological factors to improve the accuracy of preoperative prediction for lymph node metastasis (LNM) in rectal cancer. Methods A total of 191 rectal cancer patients who received treatment at Zhongshan Hospital (Xiamen), Fudan University from January 2022 to December 2024 (modeling group, n＝141) and at Zhongshan Hospital, Fudan University from January to June 2021 (external validation group, n＝50) were retrospectively included. Magnetic resonance imaging and clinical data of all patients were collected to compare the diagnostic performance of the previously developed algorithm with that of two radiologists. Logistic regression was further employed to identify independent risk factors for LNM, and an integrated model combining ‘‘algorithm + clinicopathological features’’ was constructed. The clinical value of this model was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Results In the modeling group, the algorithm’s predictive performance for LNM (AUC＝0.763) was higher than those of two physicians (AUC＝0.675, 0.655). Multivariate analysis results indicated that carcinoembryonic antigen ＞5 μg/L and low differentiation were independent risk factors for LNM (P＜0.01). The fusion model improved predictive performance, with an AUC of 0.872 in the modeling group and 0.830 in the external validation group. DCA analysis showed that the fusion model provided higher net benefits across the clinically common threshold range. Conclusions The fusion model based on multi-parametric magnetic resonance image deep learning algorithm and clinical features shows good predictive performance for LNM of rectal cancer, which is superior to simple algorithms and manual interpretation. It is expected to become an auxiliary tool for preoperative risk stratification and treatment strategy selection of rectal cancer.

ObjectiveSeveral molecular markers were evaluated for their reliability in detecting Angiostrongylus cantonensis in wild rodent feces and for determining the infection status of these rodents. MethodsA total of 128 wild rodents were captured in Huashan Town， Huadu district， Guangzhou. Their fecal samples were collected and total DNA was extracted with the FastDNA SPIN Kit for Soil. PCR amplification was performed to obtain cytb， cox I and ITS2 gene fragments， followed by Sanger sequencing and phylogenetical analysis. ResultsAmong all 128 fecal samples， only 5 had clean PCR bands and unambiguous cytb sequences suitable for reliable species identification. The infection rate of A. cantonensis was 7.58% （5/66） in Rattus norvegicus， whereas no cytb fragments were detected in Rattus tanezumi， Rattus losea， Suncus murinus and Bandicota indica. These data indicate that R. norvegicus is a highly suitable host for A. cantonensis， and the cytb gene serves as an effective molecular marker for detecting A. cantonensis in wild rodent feces. ConclusionThe cytb is an effective molecular marker for detecting A. cantonensis in wild rodent feces， enabling long-term surveillance of its prevalence and potential public health threat.

The recombinant human thyroid-stimulating hormone (rhTSH) can be used for differentiated thyroid cancer (DTC) preparation before ¹³¹I therapy, and follow-up. Since its approval by the American Food and Drug Administration (FDA) in 1998, rhTSH has been clinically used in European countries, the United States, and other countries for nearly 30 years. In April 2024, China’s first independently developed rhTSH agent was approved. Based on clinical experience and evidence from domestic and international studies, an expert consensus on the application of rhTSH has been developed following thorough consultations among experts. The consensus offers evidence-based recommendations to guide the appropriate use of rhTSH in clinical practice.

Objective To establish a rapid analysis method for cyanide based on a ratiometric fluores-cent probe,providing a quantitative strategy for on-site visual and rapid detection of cyanide.Methods A dual-emission ratiometric fluorescent probe(AuNCs-FL)was constructed by using bovine serum al-bumin(BSA)-stabilized gold nanoclusters(AuNCs,fluorescence emission at 660 nm)as the responsive signal unit and fluorescein(FL,emission at 515 nm)as the internal reference.Results The etching effect of cyanide on AuNCs resulted in fluorescence quenching at 660 nm,while the fluorescence inten-sity of FL at 515 nm remained unchanged,enabling a rapid response analysis of cyanide shift from red to green fluorescence.The developed probe enabled rapid analysis of cyanide within 3 min,with a limit of detection(LOD)of 3.4 mg/L and a visual detection range of 10-100 mg/L.Conclusion The AuNCs-FL fluorescent probe is structurally simple,low-cost,and easy to operate,delivering rapid and accurate results.It also avoids the interference from sulfides encountered in commercial cyanide test kits,making it suitable for the on-site rapid detection of suspected powder samples in cyanide poisoning cases.

Objective:To explore the effect of Lactobacillus reuteri on testicular injury in mice exposed to neonicotinoid insec-ticides(NNI).Methods:Fifteen C57BL/6 male mice were randomly divided into control group(CTRI.group),exposure group(NNI group)and Lactobacillus intervention group(NNI-L group).The mice in CTRL group were given 0.02ml/g of 0.5％carboxym-ethyl cellulose sodium solution by gavage for 14 days.The mice in NNI group were given 0.02 ml/g of NNI mixture by gavage for 14 days.The mice in NNI-L group were given 0.02 ml/g of NNI mixture by gavage and 5 × 108cfu/ml of Lactobacillus reuteri powder so-lution for 14 days.Then,the histomorphology and function of testicle were evaluated by hematoxylin-eosin staining,immunofluores-cence staining and RNA sequencing.Results:Compared with CTRL group,the thickness of testicular seminiferous epithelium in the NNI group was significantly thinner.And the decline in the number of spermatogenic cells and sperm was observed.And the expression of spermatogonial stem cell marker UCHL1 was down-regulated which was significantly improved in NNI-L group compared with the NNI group.The abnormal expressions of hormone and sperm methylation related genes in testis of NNI group were detected by RNA sequen-cing,with significant down-regulation being found in NPFF and IGF2.While the expression of HSD3B8 was significantly up-regulated.The abnormal expression of these genes could be significantly improved after oral administration of Lactobacillus reuteri.Conclusion:Testicular spermatogenesis and endocrine function can be damaged by NNI exposure.And oral administration of Lactoba-cillus reuteri protects testis from the adverse effects of NNI toxicity.

Objective To investigate the classification capability of a deep learning classification model based on biparametric mag-netic resonance imaging(bpMRI)for clinically significant prostate cancer(csPCa)and clinically insignificant prostate cancer(cisPCa).Methods A retrospective analysis was conducted on the data of 565 prostate bpMRI patients.A deep learning classification model was established for csPCa.The patients were randomly divided into training set(452 cases)and internal test set(113 cases)at a ratio of 8︰2.Internal validation was performed,followed by external validation(external validation set)using data from 120 patients across four different hospitals.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve,F1 score,precision,sensi-tivity,specificity,accuracy,and calibration curves were used to evaluate the model.Decision curve analysis(DCA)was also applied to assess the clinical benefit of the model.Results The deep learn-ing classification model for csPCa classification demonstrated the following performance across the training set,internaltest set,and external validation set:sensitivity of 0.986,0.887,and 0.750;specificity of 0.967,0.850,and 0.976;precision of 0.963,0.839,and 0.818;accuracy of 0.974,0.862,and 0.792;F1 score of 0.974,0.862,and 0.783;and AUC of 0.998,0.896,and 0.883,respec-tively.The calibration curves for all three datasets showed high consistency between predicted and actual probabilities.DCA indicated that the highest net benefit threshold probabilities for the training set,internal test set,and external validation set were 0.2-0.7,0.2-0.6,and 0.2-0.5,respectively.Conclusion The deep learning classification model demonstrated excellent performance in classifying csPCa and exhibited good generalizability,which is worhty of clinical application.

Objective To investigate the value of gemstone spectral CT multiparameter for risk assessment in acute pulmonary embolism(APE).Methods A total of 83 patients diagnosed with APE were categorized into three groups based on the European Society of Cardiology(ESC)guidelines:high-risk group(n=23),medium-high-risk group(n=29),and medium-low-risk group(n=31).The spectral CT multiparameters for each group were subsequently analyzed and compared.The predictive value of the region of interest perfusion defect iodine group value(ROI vPD),whole lung mean perfusion iodine group value(vMeanIP),and lung perfusion defect volume ratio(rPDvol)in high-risk APE patients were assessed using receiver operating characteristic(ROC)curves.Pearson correlation was employed to analyze the correlation of gemstone spectral CT multiparameter with pulmonary artery obstruction index(PAOI)and right ventricle/left ventricle diameter ratio(rRVD/LVD).Results Whole lung minimum perfusion iodine group value(vMinIP),whole lung maximum perfusion iodine group value(vMaxIP),vMeanIP,rPDvol and right ventricle/left ventricle volume ratio(rRVV/LVV)exhibited significant differences across all groups.The area under the curve(AUC)for ROI vPD,vMeanIP,and rPDvol in high-risk APE patients were 0.792,0.831,and 0.884,respectively.The sensitivity and specificity for ROI vPD(≤0.3),vMeanIP(≤1.1),and rPDvol(≥23.7％)were recorded at 95.7％and 60.0％,78.3％and 75.0％,as well as 91.3％and 75.0％,respectively.Simultaneously,the gemstone spectral CT multiparameter exhibited correlations with PAOI and rRVD/LVD.Conclusion The gemstone spectral CT multiparameter can be utilized to evaluate the severity and progression of patients with APE.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.