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.

Objective To investigate the role of SLC7A11-mediated disulfidptosis in regulating the functional heterogeneity of cancer-associated fibroblasts (CAFs) and remodeling the immune microenvironment in ovarian cancer (OC). Methods Utilizing an integrative multi-omics framework, we synchronized transcriptomic data from TCGA and GEO with high-resolution single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics. We employed CellChat interactome algorithms to map disulfidptosis-related gene expression profiles across various cellular hierarchies. The mechanistic link between SLC7A11 and SPP1 was further examined via qPCR assays under glucose-deprived, disulfidptosis-inducing conditions. Results SLC7A11 was identified as a crucial regulator of disulfidptosis, exhibiting significant overexpression in OC tissues. Elevated SLC7A11 levels were positively correlated with advanced FIGO stages, increased tumor burden, and resistance to immune checkpoint blockade. scRNA-seq profiling identified a distinct CAF lineage (CAF1/CAF5) characterized by a robust disulfidptosis signature. This subpopulation was associated with epithelial-mesenchymal transition and an immunosuppressive niche via the SPP1-CD44 signaling axis. In situ spatial mapping suggested the localized amplification of SPP1-mediated signaling at the CAF-tumor cell interface, forming high-intensity interactomes with ITGAV, ITGB1, and CD44. Metabolic stress induced by glucose starvation significantly altered the transcriptional profiles of SLC7A11 and SPP1. Notably, the glucose starvation-induced upregulation of NRF2 mRNA in fibroblasts was effectively mitigated by NRF2 knockdown or treatment with the disulfidptosis inhibitor 2-mercaptoethanol. Conclusion SLC7A11-mediated disulfidptosis may activates a subset of CAFs, which may contribute to remodeling the tumor microenvironment and suppressing anti-tumor immunity through the secretion of SPP1.

OBJECTIVE: To investigate chronic hepatitis C virus (HCV) infection's effect on gestational liver function, pregnancy and delivery complications, and neonatal development. METHODS: A total of 157 HCV antibody-positive (anti-HCV[+]) and HCV RNA(+) patients (Group C) and 121 anti-HCV(+) and HCV RNA(-) patients (Group B) were included as study participants, while 142 anti-HCV(-) and HCV RNA(-) patients (Group A) were the control group. Data on biochemical indices during pregnancy, pregnancy complications, delivery-related information, and neonatal complications were also collected. RESULTS: Elevated alanine aminotransferase (ALT) rates in Group C during early, middle, and late pregnancy were 59.87%, 43.95%, and 42.04%, respectively-significantly higher than Groups B (26.45%, 15.70%, 10.74%) and A (23.94%, 19.01%, 6.34%) ( P < 0.05). Median ALT levels in Group C were significantly higher than in Groups A and B at all pregnancy stages ( P < 0.05). No significant differences were found in neonatal malformation rates across groups ( P > 0.05). However, neonatal jaundice incidence was significantly greater in Group C (75.16%) compared to Groups A (42.25%) and B (57.02%) ( χ ² = 33.552, P < 0.001). HCV RNA positivity during pregnancy was an independent risk factor for neonatal jaundice ( OR = 2.111, 95% CI 1.242-3.588, P = 0.006). CONCLUSIONS Chronic HCV infection can affect the liver function of pregnant women, but does not increase the pregnancy or delivery complication risks. HCV RNA(+) is an independent risk factor for neonatal jaundice.
Humans ; Female ; Pregnancy ; Adult ; Pregnancy Complications, Infectious/epidemiology* ; Retrospective Studies ; Pregnancy Outcome ; Infant, Newborn ; Viremia/virology* ; Hepatitis C ; Hepacivirus/physiology* ; Hepatitis C, Chronic/virology* ; Young Adult ; Alanine Transaminase/blood*

Objective To investigate the rate of greater omentum metastasis in gastric cancer(GC). Methods General informations of patients with GC who underwent radical gastrectomy at Shanghai Ruijin Hospital in May 2020 were collected, and their clinicopathological characteristics were analyzed to find risk factors of greater omentum metastasis. Recurrence and survival were also assessed. Results A total of 59 patients with GC were included in the study, of which 2(3.4%) had greater omentum metastasis. One patient presented a pathological stage of pT4aN3bM0 and another ypT4bN1M0. The 3-year overall survival rate of patients in the study was 87.9%. Conclusions The rate of greater omentum metastasis was relatively low, and patients with greater omentum metastasis had an more advanced pathological stage. To further validate this clinical issue, a prospective randomized controlled clinical study should be conducted between radical gastrectomy with omentectomy and omentum-preserving radical gastrectomy.

Objective To analyze risk factors for sleep disorders in patients with chronic heart failure(CHF)and construct a nomogram prediction model.Methods Using simple random sampling,306 hospital-ized CHF patients meeting inclusion criteria were enrolled from four Grade A tertiary hospitals in Guangxi Zhuang Autonomous Region(two in Nanning,one each in Yulin and Guilin)between March 2023 and March 2024.LASSO regression analysis was initially employed for variable screening,followed by logistic regression to identify predictive variables for constructing the nomogram model.Model validation and performance evalua-tion were conducted using receiver operating characteristic(ROC)curves,calibration curves,and clinical decision curves,with internal validation performed through Bootstrap resampling(1 000 iterations).Results The incidence of sleep disorders among the 306 patients was 57.5%(176/306).Logistic regression analysis identified eight independent risk factors for sleep disorders in CHF patients(P＜0.05):age,education level,monthly house-hold income per capita,NYHA cardiac function classification,number of comorbidities,triglyceride levels,ano-rexia,and anxiety.The model demonstrated good discrimination for the AUC of 0.91(95%CI:0.77-0.88)and calibration consistency.Conclusion The prediction model established in this study shows good predictive performance,serving as a valuable reference for healthcare providers to early identify sleep disorders and im-plement preventive care strategies in patients with CHF.

Cosmetics may be an important source of human exposure to per-and polyfluoroalkyl substances(PFASs),posing risks to human health.In this study,a nontarget screening method for PFASs in cosmetics was developed using ultra-high performance liquid chromatography-high-resolution mass spectrometry(UHPLC-Q-Orbitrap HRMS)based on the Kendrick mass defect(KMD).The sample was extracted by ultrasonic assisted extraction prior to being analyzed by UHPLC-Q-Orbitrap HRMS.Acquisition of HRMS data was achieved in both full scan and data-dependent(Full MS/dd MS2)mode.The data collected by HRMS were imported into an in-lab built R script for processing.Samples retained the mass spectra peaks with KMD values in the range of 0.85-1 or 0-0.15 for in-and out-of-library matching;when KMD deviation(δKMD)<0.001 and CF 2 mass error(δMS)<15 ppm,it was considered as a potential PFASs homologues.According to matches of parent ions(MS),fragment ions(MS2)and retention time(RT)with the in-house built PFASs database,the screened and identified potential PFASs were categorized to 5 confidence levels(CL1-CL5).A total of 15 kinds of PFASs homologues with confidence level of CL3 and above were screened from 13 cosmetics products and 8 cosmetic raw materials,including perfluoroalkyl alcohol,hydroperfluoroalkyl sulfonic acid,chloroperfluoroalkyl sulfonic acid,etc.with concentrations ranging from 1.9 ng/g to 98.1 ng/g.The nontarget screening method could be used to screen and identify PFASs homologues feasibly and therefore provided data basis for management and control of PFASs addition in cosmetics.

In this work,with tris(4-aminophenyl)amine(TAPA)and 1,3,5-tris(4-formylphenyl)benzene(TFPB)as monomers,an imine-type porous organic polymer,TAPA-TFPB,was synthesized using a simple method under the catalysis of acetic acid.The material TAPA-TFPB was used as solid-phase extraction adsorbent and combined with ultra-performance liquid chromatography/quadrupole time-of-flight-tandem mass spectrometry(UHPLC-QTOF-MS)to establish a detection method for four kinds of nitrofuran metabolites(NFMs)residues in meat samples.The parameters of the adsorbent dosage,the pH value and volume of sample,and the type and volume of washing and eluent solvents were optimized,respectively.Under the optimal extraction conditions,low detection limits(0.11-1.60 μg/kg)were achieved for four kinds of NFMs.At three different spiked levels,the intra-day and inter-day precisions(Relative standard deviations)were 2.8% -10.9% and 4.3% -16.2%,respectively,and the spiked recoveries were 72.0% -107.2%.The results showed that the method chould be used for efficient extraction and analysis of trace NFMs residues in meat samples,indicating that TAPA-TFPB was a kind of promising SPE adsorbent.

In this work,near infrared carbon quantum dots(NIR-CDs)were synthesized by hydrothermal method using biomass material Clausena lansium leaves.The synthesized NIR-CDs emitted maximum fluorescence signal at 677 nm,which was independent of excitation wavelength.The characterization results showed that there were abundant groups on the surface of NIR-CDs.Pd2+could form non-fluorescent compounds with the surface groups of NIR-CDs,resulting in fluorescence quenching(Fluorescence signal was denoted as F0).Because chlorpromazine hydrochloride(CPZ)parent nucleus contained unoxidized S atom,CPZ could form stable colored complex with Pd2+under acidic conditions.In the presence of CPZ,Pd2+dissociated from the surface of NIR-CDs and bonded with CPZ,so that the fluorescence signal could be restored(Fluorescence signal was denoted as F).An"on-off-on"fluorescence sensor was thus constructed.The fluorescence signal recovery value of NIR-CDs(△F=F-F0)showed a good linear relationship with the concentration of CPZ in the range of 5.68-28.43 μg/mL,and the detection limit(3σ)was 0.078 μg/mL.The sensor was applied to determination of CPZ in pharmaceutical preparations,and the recoveries were 94％-106％.The developed fluorescence sensor was expected to be used in quality control of actual pharmaceutical preparations.

Objective To construct a competency evaluation model for forensic practitioners,providing a reference for their training and assessment.Methods Based on the iceberg and onion models of com-petency,and with reference to Spencer's Competency Dictionary,literature research was conducted and focus group interviews were employed to preliminarily construct core indices and measurement items for evaluating the competency of forensic practitioners.The Delphi method was applied for two rounds of expert consultation to further refine the competency evaluation index system.The analytic hierarchy process(AHP)was used to calculate the weights of the indices.Results A competency evaluation model for forensic practitioners was constructed,consisting of 7 core indices,encompassing forensic skills,identification service capabilities,and the ability to apply relevant legal knowledge and 49 mea-surement items.The weights of the core indices and measurement items were determined.Conclusion The constructed competency evaluation model for forensic practitioners is scientifically sound and inno-vative,and has unique characteristics of forensic medicine compared with other medical models.