OBJECTIVE To introduce the development of an intelligent prescription review decision-support system for anti-tumor drugs and assess its clinical application outcomes. METHODS Relevant data sources， including national and local pharmaceutical administration policies， clinical practice guidelines/consensus， hospital information systems data， and genetic testing results， were integrated. Adhering to the principles of structure， standardization and dynamic updating， a knowledge base covering chemotherapeutic， targeted and immunotherapeutic agents was constructed using a dual-dimensional modeling approach that combined “drug attributes” and “clinical contexts”. This knowledge base was then embedded into the hospital’s electronic medical order system to establish the prescription review decision-support system. The application and performance of the system were evaluated at Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. RESULTS A knowledge base containing 18 318 prescription review rules for anti-tumor drugs was constructed， and a closed-loop prescription review system was successfully established， encompassing pre-prescription real-time intervention， in-process interactive review， and post-prescription evaluation and analysis. From 2021 to 2024， the system generated a total of 57 879 alerts for prescriptions of five typical categories of anti-tumor drugs. For platinum-containing prescriptions， 22 577 alerts were generated， with Cisplatin for injection （lyophilized） being the most frequently alerted drug （13 445 alerts）， and “ototoxicity risk due to combined use” alerts remained high （7 682 alerts）. For methotrexate-containing prescriptions， 3 721 alerts were recorded， primarily related to “precaution-related issues” （76.4%， 2 843/3 721）. For doxorubicin-containing prescriptions， 17 301 alerts were triggered， primarily related to “dosage and administration” （14 315 alerts）. For human epidermal growth factor receptor 2-targeted agents-containing prescriptions， 1 007 alerts were issued， mostly related to “reimbursement restrictions” （956 alerts）. For programmed death-1/programmed death-ligand 1 inhibitors-containing prescriptions， the alerts increased year by year， totaling 13 273 alerts， primarily related to “inappropriate indication” （9 118 alerts）. Over the 4 years， the physician response rates to system alerts were 21.4%， 27.1%， 33.5% and 51.6%， respectively. CONCLUSIONS An intelligent decision-support system for anti-tumor drug prescription review， encompassing a closed-loop process of “real-time pre-event intervention， interactive in-event prescription review， post-event evaluation and analysis”， has been successfully constructed and implemented throughout the entire workflow. There is a discernible trend in this hospital， where the focus on monitoring anti-tumor drugs is shifting towards immunotherapy drugs. Additionally， the acceptance rate of physicians regarding prescription review opinions has been steadily increasing year by year.

Immune checkpoint inhibitors (ICIs) are widely used in the treatment of malignant tumors, and their related immune-related adverse events (irAEs) have attracted increasing attention. This study reports the diagnosis and treatment process of a case of immune cystitis in a patient with hepatobiliary tract malignant tumor after treatment with pembrolizumab. The patient was admitted to the hospital due to frequent urination, urgency of urination and dysuria for 1 month. Previous repeated anti-infection treatments were ineffective. Combined with medical history, laboratory tests, imaging findings, cystoscopy and pathological results, the patient was clinically diagnosed with ICIs-associated immune cystitis (Pembrolizumab) ultimately. The patient's symptoms significantly improved after treatment with glucocorticoids. This case reindicates that clinicians need to improve awareness of ICI-related urinary system irAEs. Early identification and timely intervention can significantly improve patient prognosis.

ObjectiveThis paper aims to investigate the material basis of the anti-inflammatory efficacy and mechanism of action of Bushen Tongdu prescription （BSTDP）. MethodsThe chemical components of BSTDP and its blood-absorbed components in vivo were systematically identified by using ultra-performance liquid chromatography-linear ion trap-electrostatic field orbitrap high-resolution mass spectrometry （UPLC-LIT-Orbitrap-MS）. Network pharmacology was employed to screen blood-absorbed bioactive components and potential targets of this formula. A protein-protein interaction （PPI） network of core targets was constructed to conduct enrichment analysis. Molecular docking was further utilized to verify the binding affinity between key components and targets. The inflammatory model was established and verified in vivo by using a transgenic zebrafish Tg （mpx： GFP）. At three days post-fertilization （3 dpf）， larvae of zebrafish were randomly assigned to blank group， model group， positive drug dexamethasone acetate group （75 μmol·L^-1）， and BSTDP groups with low， medium， and high doses （500， 1 000， and 2 000 mg·L^-1）. The distribution and quantity of neutrophils in the yolk sac region were observed under a fluorescence microscope. The mRNA expression levels of key genes in the toll-like receptor 4 （TLR4）/myeloid differentiation factor 88 （MyD88）/nuclear factor kappa-B （NF-κB） signaling pathway and inflammatory factors including interleukin （IL）-1β， IL-6， and tumor necrosis factor-α （TNF-α） were detected by Real-time quantitative polymerase chain reaction （Real-time PCR）. ResultsA total of 120 chemical components were identified in BSTDP， among which 26 original components were confirmed by using serum pharmacochemical methods. A total of 227 common targets linking rheumatoid arthritis （RA） and the blood-absorbed components were screened by network pharmacology. It is suggested that pseudobrucine， vomicine， sinapine， rehmannioside， cinnamyl alcohol glycoside， and methylephedrine exert anti-inflammatory effects by acting on core targets including protein kinase B1 （Akt1）， signal transducer and activator of transcription 3 （STAT3）， tumor necrosis factor （TNF）， TLR4， mitogen-activated protein kinase 14 （MAPK14）， and phosphatidylinositol-4，5-bisphosphate 3-kinase catalytic subunit α （PIK3CA）， thereby modulating multiple signaling pathways such as TLR4 and NF-κB. In vivo verification in zebrafish demonstrates that the maximum tolerable concentration of Bushen Tongdu Formula is 2 000 mg·L^-1. Compared to those in the blank group， zebrafish in the model group showed a significantly higher number of neutrophils in the yolk sac region （P<0.01） and rising mRNA levels of TLR4， MyD88， NF-κB， TNF-α， IL-6， and IL-1β （P<0.01）. Compared to that in the model group， the number of neutrophils was significantly reduced in BSTDP groups with medium and high doses， as well as the dexamethasone acetate group （P<0.05， P<0.01）. There was no statistically significant difference in the low dose group. The mRNA expression levels of TLR4， MyD88， NF-κB， TNF-α， IL-6， and IL-1β were significantly down-regulated （P<0.05， P<0.01）. ConclusionThis paper identifies the material basis of the efficacy of BSTDP， demonstrating that the formula can exert an anti-inflammatory effect through the TLR4/MyD88/NF-κB signaling pathway. The results provide scientific experimental evidence for its further clinical application.

ObjectiveThis paper aims to investigate the material basis of the anti-inflammatory efficacy and mechanism of action of Bushen Tongdu prescription （BSTDP）. MethodsThe chemical components of BSTDP and its blood-absorbed components in vivo were systematically identified by using ultra-performance liquid chromatography-linear ion trap-electrostatic field orbitrap high-resolution mass spectrometry （UPLC-LIT-Orbitrap-MS）. Network pharmacology was employed to screen blood-absorbed bioactive components and potential targets of this formula. A protein-protein interaction （PPI） network of core targets was constructed to conduct enrichment analysis. Molecular docking was further utilized to verify the binding affinity between key components and targets. The inflammatory model was established and verified in vivo by using a transgenic zebrafish Tg （mpx： GFP）. At three days post-fertilization （3 dpf）， larvae of zebrafish were randomly assigned to blank group， model group， positive drug dexamethasone acetate group （75 μmol·L^-1）， and BSTDP groups with low， medium， and high doses （500， 1 000， and 2 000 mg·L^-1）. The distribution and quantity of neutrophils in the yolk sac region were observed under a fluorescence microscope. The mRNA expression levels of key genes in the toll-like receptor 4 （TLR4）/myeloid differentiation factor 88 （MyD88）/nuclear factor kappa-B （NF-κB） signaling pathway and inflammatory factors including interleukin （IL）-1β， IL-6， and tumor necrosis factor-α （TNF-α） were detected by Real-time quantitative polymerase chain reaction （Real-time PCR）. ResultsA total of 120 chemical components were identified in BSTDP， among which 26 original components were confirmed by using serum pharmacochemical methods. A total of 227 common targets linking rheumatoid arthritis （RA） and the blood-absorbed components were screened by network pharmacology. It is suggested that pseudobrucine， vomicine， sinapine， rehmannioside， cinnamyl alcohol glycoside， and methylephedrine exert anti-inflammatory effects by acting on core targets including protein kinase B1 （Akt1）， signal transducer and activator of transcription 3 （STAT3）， tumor necrosis factor （TNF）， TLR4， mitogen-activated protein kinase 14 （MAPK14）， and phosphatidylinositol-4，5-bisphosphate 3-kinase catalytic subunit α （PIK3CA）， thereby modulating multiple signaling pathways such as TLR4 and NF-κB. In vivo verification in zebrafish demonstrates that the maximum tolerable concentration of Bushen Tongdu Formula is 2 000 mg·L^-1. Compared to those in the blank group， zebrafish in the model group showed a significantly higher number of neutrophils in the yolk sac region （P<0.01） and rising mRNA levels of TLR4， MyD88， NF-κB， TNF-α， IL-6， and IL-1β （P<0.01）. Compared to that in the model group， the number of neutrophils was significantly reduced in BSTDP groups with medium and high doses， as well as the dexamethasone acetate group （P<0.05， P<0.01）. There was no statistically significant difference in the low dose group. The mRNA expression levels of TLR4， MyD88， NF-κB， TNF-α， IL-6， and IL-1β were significantly down-regulated （P<0.05， P<0.01）. ConclusionThis paper identifies the material basis of the efficacy of BSTDP， demonstrating that the formula can exert an anti-inflammatory effect through the TLR4/MyD88/NF-κB signaling pathway. The results provide scientific experimental evidence for its further clinical application.

Objective: To analyze the impact of premature death due to four major chronic diseases on life expectancy in Yangpu District, Shanghai Municipality from 2010 to 2021, so as to provide the evidence for formulating chronic disease prevention and control strategies. Methods : Mortality data of registered residents in Yangpu District from 2010 to 2021 were collected through the Death Information Registration and Management System of the Shanghai Municipal Disease Control and Prevention Information Management Platform. The premature death probability of malignant tumors, diabetes, cardiovascular and cerebrovascular diseases, and chronic respiratory diseases, and life expectancy of residents were calculated using the abridged life table method. Trends in premature death probability for four types of chronic diseases were analyzed using the average annual percent change (AAPC). The impact of premature death probability due to four chronic diseases on life expectancy was assessed by Arriaga's decomposition method. Results : The premature death probability due to four major chronic diseases in Yangpu District decreased from 9.88% in 2010 to 9.22% in 2021, showing an overall declining trend (AAPC=-0.540%, P<0.05). Among females, the premature death probability declined from 6.71% to 4.90% (AAPC=-2.715%, P<0.05), whereas no statistically significant trend was observed in males (P>0.05). Life expectancy increased from 82.52 years in 2010 to 84.50 years in 2021, with an overall upward trend (AAPC=0.244%, P<0.05). Life expectancy rose by 1.71 years for males and 2.34 years for females (AAPC=0.197% and 0.303%,both P<0.05). Declines in premature death probability from malignant tumors (AAPC=-0.967%, P< 0.05) and chronic respiratory diseases (AAPC=-3.071%, P<0.05) contributed to gains in life expectancy of 0.30 years and 0.03 years, with contribution rates of 12.18% and 1.29%, respectively. Changes in premature death probability due to diabetes as well as cardiovascular and cerebrovascular diseases were not statistically significant (both P>0.05), resulting in reductions in life expectancy of 0.05 years and 0.10 years, with contribution rates of -2.40% and -5.05%, respectively. Notably, an increase in premature death probability due to cardiovascular and cerebrovascular diseases among males (AAPC=1.673%) contributed to a decrease of 0.22 years in male life expectancy, whereas a decrease among females (AAPC=-3.824%) contributed to an increase of 0.03 years in female life expectancy, with contribution rates of -13.03% and 1.14%, respectively. Conclusions From 2010 to 2021, Yangpu District experienced an overall decline in premature death probability due to four major chronic diseases and an increase in life expectancy. Greater attention should be paid to the negative impacts of premature death probability from diabetes as well as cardiovascular and cerebrovascular diseases among males on life expectancy.

OBJECTIVE To provide reference for medical institutions to establish the record management mode and review rules of off-label use of 5-aminolevulinic acid （ALA） in photodynamic therapy based on the level of evidence. METHODS All ALA-containing outpatient prescriptions in the rational drug use system in our hospital from January 1， 2024 to December 31， 2025 were retrospectively collected. Based on the drug instructions， the current status of off-label use of ALA in photodynamic therapy was identified . The relevant studies in Micromedex， PubMed， CNKI， Wanfang Data and other databases were systematically searched as the relevant evidence-based evidence of ALA off-label use. According to the Off-label Drug Use Filing Standard of the hospital，the evidence-based evaluation method was used to evaluate the evidence-based evidence of ALA off-label use and carry out hierarchical management. RESULTS A total of 1 803 effective prescriptions were included， of which 676 （37.49%） were off-label use， distributed in the dermatology department （564 prescriptions，83.43%） and the plastic surgery department （112 prescriptions，16.57%）. All 676 prescriptions were off-indications medication， involving ten types of skin diseases， primarily including moderate to severe acne （39.94%）， skin warts （25.44%）， Bowen’s disease （11.98%）， and others. According to evidence-based evidence，off-label uses such as moderate to severe acne， actinic keratosis， and Bowen’s disease were managed according to the evidence categoryⅠ orⅡ.The uses of extramammary Paget’s disease and rosacea were managed according to the evidence category Ⅲ.The uses of lichen sclerosus and keloids were managed according to the evidence category Ⅳ.The results of evidence-based evaluation showed that 92.01% of off-label use in our hospital had high-level evidence-based support （ evidence category was gradeⅠ-Ⅱ）. CONCLUSIONS Off-label uses supported by high-level evidence， such as moderate to severe acne， skin warts， and Bowen’s disease， can be managed under filing category Ⅰ or Ⅱ. For the use of lichen sclerosus and keloids， evidence-based evidence is insufficient and should be strictly restricted.The vast majority of ALA off-label use in our hospital has sufficient evidence-based basis.

ObjectiveTo analyze the research hotspots and development trends in the field of spinal cord stimulation (SCS) for spinal cord injury (SCI). MethodsLiterature about SCS for SCI was retrieve from the Web of Science (WOS) Core Collection database, with a time range from January, 1999 to July, 2025. VOSviewer 1.6.20 and CiteSpace 6.4.R2 were used to analyze the annual publication volume, countries, authors, institutions, journals and keywords. ResultsA total of 636 literatures were included. From 1999 to 2025, the overall publication trend in this field showed an upward trajectory, with recent years fluctuating but tending to stabilize. The country with the most publications was the United States (429 papers), followed by Russia (98 papers) and China (70 papers). The institution with the highest number of publications was the University of California, Los Angeles (76 papers), the author with the most publications was V. Reggie Edgerton (70 papers), and the journal with the most publications was Journal of Clinical Medicine (31 papers). The most frequently cited study focused on exploring the combination of epidural spinal cord stimulation with task-specific training to restore motor function in patients with complete SCI. Keyword analysis showed that the research hotspots in this field were mainly focused on neuroregulation mechanisms, recovery of motor and autonomic nervous dysfunction, artificial intelligence, closed-loop stimulation and brain-computer interface technology innovations. In recent years, the research focus gradually shifted from basic mechanisms to personalized and precise multifunctional rehabilitation strategies. ConclusionThe field of SCS for SCI has undergone phases of basic mechanism exploration and clinical application expansion. Current research hotspots and future trends focus primarily on the development of new stimulation paradigms and combined innovative technologies.

Optical microscopy is essential for exploring biological and material structures, with resolution determining the level of observable detail. The advent of super-resolution fluorescence microscopy has broken the diffraction limit, achieving nanoscale resolution. However, traditional assessment methods, such as the Rayleigh criterion and point spread function (PSF) width measurement, rely on empirical judgments and diffraction-limited models, rendering them inadequate for modern super-resolution imaging. This review systematically traces the evolution of resolution assessment methodologies, from classical criteria to advanced strategies tailored for various super-resolution modalities. We first discuss Fourier-based quantitative methods. Fourier ring correlation (FRC) and its 3D counterpart, Fourier shell correlation (FSC), objectively determine resolution by evaluating the statistical correlation of two independent image reconstructions in frequency space. These methods offer robustness against noise and provide a global resolution metric, but they require data independence and are computationally intensive. They have become the prevailing standards in electron and super-resolution microscopy. Subsequently, we examine adaptations for specific super-resolution techniques. For single-molecule localization microscopy (SMLM) techniques such as PALM and STORM, the Fourier image resolution (FIRE) method extends FRC by incorporating a physical model that accounts for localization precision and labeling density. For stimulated emission depletion (STED) microscopy and other nonlinear techniques, assessment strategies differ. While PSF shrinkage measurements using fluorescent beads are useful for system calibration, evaluating the effective resolution directly on biological samples is more practical. This is typically performed via linewidth analysis of known structures (e.g., microtubules) or edge-spread function measurements, capturing the effects of photobleaching and sample-induced aberrations. A major paradigm shift is parameter-free resolution estimation based on decorrelation analysis. This method analyzes the autocorrelation decay of a single image’s Fourier spectrum to identify the cutoff spatial frequency without requiring dual datasets or user-defined thresholds. Its high efficiency and broad applicability have been validated across widefield, confocal, STED, SIM, and SMLM modalities. Optimized rendering strategies for SMLM data further enhance its accuracy, and it is emerging as a tool for real-time optimization of experimental parameters. The review also addresses the “gold standard” of resolution validation using well-defined nanostructures, such as DNA origami and nuclear pore complexes, which provide ground truth for verifying resolution claims and detecting artifacts. In the era of artificial intelligence, deep learning plays a dual role: it powerfully enhances image resolution but also introduces challenges, as models may generate “hallucinations” or false details. This underscores the need for new validation metrics to verify the physical fidelity of AI-generated content. Finally, we outline future directions: developing unified cross-modality standards, enabling real-time dynamic resolution monitoring for live-cell imaging, creating techniques for generating local resolution maps to capture sample heterogeneity, and integrating intelligent error correction to ensure data veracity. By providing a comprehensive overview of resolution assessment progress and challenges, this review aims to equip researchers with the knowledge to select appropriate tools, thereby fostering rigorous quantitative imaging in the life and material sciences.

ObjectiveThis study aims to explore the potential of different orders of magnitude single-nucleotide polymorphism (SNP) locus combinations for predicting distant kinship relationships. A high-density SNP locus set was constructed, and a comprehensive assessment of its inference capability was conducted. MethodsFirstly, we selected three commercial chip panels, CGA (Chinese genotyping array, Illumina), GSA (Global screening array, Illumina), Affy (23MF_V2 high-density SNP array, Affymetrix) and merged them after quality control, forming a high-density SNP locus panel(1 180 k). Secondly, we selected 161 samples and collected their peripheral blood samples by using whole-genome sequencing technology. Within this sample population, the levels of kinship relationships fully covered the range from level 1 to level 9, and the number of kinship pairs at each level was consistently maintained at over 50 pairs. From 161 samples data of whole-genome sequencing, the 1 180 k locus set was extracted, which is referred to as the high-density SNP locus set in the following text. The kinship inference was conducted using the identity-by-descent (IBD) algorithm with the selected optimal parameters. To comprehensively evaluate the performance of the high-density SNP locus set in kinship inference, we compared it with the three commercial chip panels, the intersection of these three chip loci, and the control sets constructed by randomly reducing the number of the high-density SNP locus set. Based on the changes in the IBD lengths, as well as the dynamic trends in prediction accuracy, we conducted a scientific assessment of the kinship inference capability of the high-density SNP locus set. ResultsAfter screening, a set of 1 184 334 autosomal SNPs was obtained. During the process of screening the optimal IBD length threshold, the result revealed that 0 cM, 1 cM, and 2 cM all demonstrated good applicability. However, to avoid the issue of a large amount of redundant information caused by setting a too low IBD length threshold, this study ultimately selected 2 cM as the optimal threshold. Compared with the average results of three chip panels, the high-density SNP locus set increased the total IBD length and the average IBD length across levels 1-9; the accuracy of the confidence interval for level 8 was 70.97%, which represented a 3.50% improvement; the average confidence interval accuracy for levels 1-8 was 91.39%, representing a 1.00% increase; and the false negative rates at levels 8 and 9 were reduced by 2.42% and 6.76%, respectively. The system efficacy of the high-density SNP locus set for kinship inference of first to eighth degree relationships reached 98.91%. Through random reduction of the high-density SNP locus set results, it is found that increasing the number of SNPs with the panel, the detection efficiency of IBD length showed a significant upward trend. At the same time, the overall trend in the accuracy of kinship relationship prediction as well as the confidence interval accuracy also indicated that both metrics steadily increased with the addition of more loci. ConclusionThe results show that the high-density SNPs panel significantly enhances the efficacy of distant kinship inference, accurately covering kinship degrees, with the average confidence interval accuracy for first to eighth degree relationships stably above 90%. The study finds that increasing the number of SNPs panel can improve the ability to predict distant kinship.

OBJECTIVE To investigate the protective effect of 2-dodecyl-6-methoxy-2，5-diene-1，4-cyclohexanedione （DMDD）， an active component from Averrhoa carambola root， on myocardial injury in diabetic mice based on the nuclear receptor coactivator 4/ferritin heavy chain 1/autophagy-related protein 8 （NCOA4/FTH1/ATG8） axis. METHODS The successfully modeled diabetic mice were randomly divided into model group and DMDD low-， medium-， and high-dose （12.5， 25， 50 mg/kg） groups， while an additional non-modeled control group was established， with 6 mice in each group. Each group received the corresponding drug solution or an equal volume of normal saline intragastically once daily for 21 consecutive days. After the administration， the levels of fasting blood glucose （FBG）， serum lactate dehydrogenase （LDH）， and creatine kinase isoenzyme MB （CK-MB） were measured. Myocardial pathological changes， degree of fibrosis， and myocardial cell ultrastructure were observed. Myocardial cell death index and NCOA4 protein positive index were detected. The protein expression levels of NCOA4， FTH1， ATG8， solute carrier family 7 member 11 （SLC7A11）， and glutathione peroxidase 4 （GPX4） in cardiac tissue were measured. RESULTS Compared with model group， each DMDD group showed significant alleviation of cardiac pathological injury and varying degrees of improvement in the myocardial cell ultrastructure. The FBG and serum LDH and CK-MB levels， the myocardial cell death index and NCOA4 protein positive index，the protein expression levels of NCOA4， FTH1， and ATG8 in cardiac tissue were significantly decreased （ P ＜0.001）， while the protein expression levels of SLC7A11 and GPX4 were significantly increased （ P ＜0.001）. CONCLUSIONS DMDD can reduce blood glucose levels， alleviate myocardial histopathological injury， and inhibit cell death in diabetic mice. The mechanism is associated with inhibiting excessive activation of the NCOA4/FTH1/ATG8 axis and reducing ferritinophagy.