Light chain amyloidosis (AL) and multiple myeloma (MM) are interrelated plasma cell disorders characterized by malignant proliferation, yet they demonstrate distinct pathophysiological mechanisms and clinical progression patterns. The clinical differentiation between these conditions presents significant challenges, frequently resulting in delayed diagnosis, particularly for AL amyloidosis, which adversely affects patient prognosis. Current diagnostic methodologies predominantly depend on invasive tissue biopsies and extended serological testing, underscoring the urgent requirement for rapid, non-invasive auxiliary diagnostic approaches. In this investigation, we developed an innovative analytical framework integrating serum Raman spectroscopy with an advanced one-dimensional convolutional neural network (1D-CNN) to achieve precise discrimination between AL and MM. Serum specimens were collected from clinically diagnosed patients and analyzed using a 785 nm excitation Raman system spanning the spectral range of 200-2000 cm-1. Following comprehensive preprocessing procedures, our specially designed 1D-CNN architecture attained exceptional classification performance, demonstrating area under the curve (AUC) values of 0.94 for AL and 0.96 for MM, with overall accuracy reaching 92.5%, accompanied by 91.4% sensitivity and 93.1% specificity. The proposed model exhibited statistically superior performance (p < 0.01) compared to conventional machine learning algorithms, including support vector machines (AUC = 0.78), and other deep learning architectures. Critical spectral analysis identified prominent Raman band variations at 500 cm-1, 1150 cm-1, and 1750 cm-1, providing molecular-level insights into the discriminatory characteristics. This spectroscopy-based deep learning platform represents a substantial advancement in clinical diagnostics, offering a rapid, non-invasive methodology with significant potential for early disease screening and enhanced decision-support in differential diagnosis of plasma cell dyscrasias.

AIMS:Size-fractionated ambient particulate matter (PM) was collected from two of the most highly PM-polluted agricultural regions in California the Imperial Valley (PMIV), and San Joaquin Valley (Parlier, PMPA), to compare the effects of particle source, size, and duration of exposure on inflammatory gene expression, cell viability, and aryl hydrocarbon receptor (AhR) activation. METHODS:Here we tested that the chemical composition of the PM would provoke different effects unique to the PM size fraction and with an association between exposure time, activation of AhR, and the expression of inflammatory genes. Human U937-derived macrophages were used to measure inflammatory biomarkers, cell viability and engulfment of PM. Particles across three size fractions - ultrafine (≥ 0.1 µm), fine (0.1-2.5 µm), and coarse (2.5-10 µm) were tested. RESULTS:Gene expression varied by PM source, size, and duration of exposure. PMIV typically induced a greater level of gene expression than PMPA of the same size fraction. For the 12-h experiments, ultrafine and coarse particle fractions were the most potent stimulators of gene expression compared to the control, irrespective of PM source. The results show that ultrafine/fine PMIV and fine PMPA typically produced the greatest increase in mRNA levels compared to the control in an AhR-dependent manner. CONCLUSIONS:Ultrafine and fine PM from both sites (PMIV and PMPA) preferentially engaged AhR-dependent signaling, whereas coarse PM activated NF-κB-mediated inflammatory pathways. Overall, this study demonstrates PM size- and time-dependent effects on inflammatory gene expression and highlights a distinction between AhR- and NF-κB-driven responses across PM fractions.

Papillary thyroid carcinoma (PTC) is the most prevalent thyroid malignancy and its incidence continues to rise. Although prognosis is generally favorable, overdiagnosis, overtreatment, and occasional lethal complications persist. To clarify the micro-environmental basis of PTC metastasis, we integrated single-cell RNA-seq from tumor (T), peritumoral thyroid (PT) and lymph-node (LN) tissues with bulk transcriptomes. Metastatic (M) and non-metastatic (NM) cases were compared, and machine-learning models were built to predict lymph-node status and outcome. Our atlas delineates highly heterogeneous ecosystems across T, PT and LN samples. Within T/NK lymphocytes, both immune-activating and immune-suppressive subsets coexist; NK and CD4+TNFRSF4 cells were selectively enriched in M tumors, implying heterogeneous anti-tumor responses. Among myeloid cells, a Mac-APOC1 subset displayed tumor-associated macrophage/M2 features, consistent with immune escape and dissemination. Dendritic cells differed markedly in antigen presentation and chemotaxis, while RGS5+ myCAFs, markedly expanded in M samples from T and LN, up-regulated metabolic and ribosomal pathways. Trajectory analysis uncovered dynamic CAF state transitions. Tumor epithelial cells followed multiple developmental branches, with those linked to LN invasion predicting poorer survival. A random-forest classifier achieved 0.98 accuracy for N-stage, and a lasso-Cox signature (C-index > 0.9) robustly stratified survival, underscoring the clinical relevance of the identified genes. Collectively, this work reveals the cellular and functional heterogeneity that drives PTC progression, identifies metastasis-associated subpopulations, and demonstrates the utility of machine learning for precise prediction of lymph-node metastasis and patient prognosis, offering a foundation for tailored therapeutic strategies.

[This corrects the article DOI: 10.1016/j.bbrep.2026.102611.].

Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy characterized by extensive genomic alterations and molecular diversity, posing significant therapeutic challenges. Current treatment strategies lack precise predictive biomarkers for chemotherapy response, highlighting the need for tools to guide precision therapy. In this study, we evaluated in vitro chemotherapy sensitivity in bone marrow samples from 98 AML patients using the PharmaFlow platform. Sensitivity to 10 commonly used chemotherapeutic agents (including venetoclax) and 20 combination regimens was assessed. Patients were stratified into three groups based on their PharmaFlow sensitivity profiles: multi-sensitive, intermediate-resistant, and multi-resistant. Analysis of these groups revealed that the rate of complete remission (CR) after one cycle of induction therapy decreased with increasing resistance. Additionally, integration of in vitro chemotherapy sensitivity data with mutational profiles indicated that DNMT3A mutations were linked to greater resistance to venetoclax, whereas CEBPA mutations in the bZIP region were linked to increased sensitivity. External validation in an independent cohort of 56 patients treated with “7 + 3” plus venetoclax confirmed a significantly lower CR rate in DNMT3A-mutant cases and a trend toward higher CR rates in CEBPA-mutant cases. Furthermore, multivariate Cox regression analysis identified multi-resistance in PharmaFlow stratification as an independent risk factor for overall survival (OS). High-throughput ex vivo drug sensitivity testing can help predict induction chemotherapy outcomes in AML. When combined with genetic mutation analysis, it helps identify mutation signatures that respond better to specific treatments, supporting the development of therapeutic strategies.

Abstract Objectives To investigate the role of super-resolution contrast-enhanced ultrasound (SR-CEUS) in evaluating inflammatory activity in Crohn’s disease (CD). Materials and methods In this prospective study, we consecutively enrolled CD patients confirmed by clinical and ileocolonoscopic findings. All patients underwent B-mode ultrasound (BMUS), color Doppler flow imaging (CDFI), CEUS, and SR-CEUS within 1 week of ileocolonoscopy. SR-CEUS quantitative parameters were recorded, with simple endoscopic score for Crohn’s disease (SES-CD) as the reference standard. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. Results 52 consecutive CD patients were categorized into active (SES-CD ≥ 3, n = 30) and inactive (SES-CD < 3, n = 22) groups. SR-CEUS clearly visualized the intramural microvascular architecture of the bowel wall. SR-CEUS yielded an AUC of 0.903 with 86.4% sensitivity (95% CI: 66.7–95.3%), and 86.7% specificity (95% CI: 70.3–94.5%) for assessing inflammatory activity, significantly outperforming both CDFI (p = 0.014) and CEUS (p = 0.045), while showing no statistically significant difference in comparison with BMUS (p = 0.988). Furthermore, the combination of BMUS and SR-CEUS achieved an AUC of 0.967 for diagnosing active CD, with 100% sensitivity (95% CI: 85.1–100%) and 86.7% specificity (95% CI: 70.3–94.7%), which was significantly superior to BMUS alone (p = 0.038). Conclusions SR-CEUS provides quantitative microvascular perfusion maps that display vascular density, flow velocity, and direction, offering a non-invasive tool for evaluating inflammatory activity in CD. Critical relevance statement This study demonstrates that super-resolution contrast-enhanced ultrasound (SR-CEUS) provides a novel, non-invasive approach for quantitative evaluation of inflammatory activity in Crohn’s disease (CD), which serves as a valuable supplement or alternative to endoscopy in routine monitoring. Key Points An unmet need remains for accurate, non-invasive tools to assess CD activity. SR-CEUS outperforms conventional CDFI and CEUS in distinguishing active from inactive CD. Combining SR-CEUS with standard BMUS yields excellent diagnostic accuracy, establishing this combined approach as a promising non-invasive alternative for monitoring inflammatory activity in CD patients. Graphical Abstract

BACKGROUND:Memory impairment is a common postoperative neurological complication among elderly patients. Sharp-wave ripples (SPW-Rs) in the CA1 region of the hippocampus play a critical role in memory consolidation. However, the extent to which disruptions in CA1 SPW-Rs contribute to postoperative memory impairment remains poorly understood. METHODS:An 18-month-old male C57BL/6J mice were exposed to 3 vol% sevoflurane combined with laparotomy. Various methodologies, including context fear conditioning, local field potential monitoring, immunofluorescence staining, viral tracing, optogenetics, and chemogenetics, were used to elucidate the involvement of SPW-Rs in the CA1 region in postoperative memory impairment in aged mice. RESULTS:Postoperative memory impairment in aged mice was associated with deficits in memory consolidation, characterised by a reduced SPW-R frequency and duration in the CA1 region. Induction of SPW-Rs had the potential to improve memory consolidation (from 34.3 [9.4] to 53.1 [18.7]%, P=0.016). Additionally, we observed a decrease in the number of c-Fos-positive pyramidal neurones in the CA3 region following surgery, which contributed to diminished excitatory transmission to the CA1 region. Activating CA3 pyramidal neurones through chemogenetic approaches restored activity in CA1 pyramidal neurones, ameliorating SPW-R disruption (frequency from 0.20 [0.05] to 0.25 [0.15] events s-1, P=0.018; duration from 0.034 [0.0028] to 0.039 [0.0033] s, P=0.014) and memory impairment. Microglial activation was associated with SPW-R disruption and postoperative memory deficits. CONCLUSIONS:Surgery triggers microglial activation, leading to the release of neuroinflammatory factors that inhibit hippocampal CA3 pyramidal neurone activation, ultimately disrupting SPW-R dynamics and impairing memory consolidation.

Preeclampsia (PE) is a major cause of maternal and perinatal morbidity and mortality worldwide, characterized by hypertension, proteinuria, and placental dysfunction. Increasing evidence implicates aberrant immune activation and vascular injury in PE pathogenesis, but the upstream signals and cellular mechanisms remain incompletely understood. We integrated transcriptomic profiling, maternal serum cytokine analysis, and placental immunohistochemistry to identify dysregulated chemokines. Human and murine trophoblasts were stimulated under hypoxia or LPS challenge to assess IL-8/CXCL1 production. Functional assays of NET formation and endothelial apoptosis were conducted in trophoblast–neutrophil–endothelium co-culture systems. Finally, we tested the therapeutic effects of neutralizing anti-CXCL1 antibody or the CXCR1/2 inhibitor SX682 in LPS-induced murine models of PE. We found that IL-8 was markedly elevated in PE patients and correlated with disease severity. Hypoxia- or LPS-stimulated trophoblasts secreted abundant IL-8/CXCL1, which recruited and activated neutrophils to undergo NETosis. NETs directly induced endothelial mitochondrial dysfunction and apoptosis, resulting in vascular injury. Pharmacological blockade of IL-8 signaling, either by CXCL1 neutralization or CXCR1/2 inhibition, significantly ameliorated hypertension, proteinuria, and fetal growth restriction in murine PE models, without altering placental weight or gross morphology. Our findings define a trophoblast–neutrophil–endothelium axis in which IL-8–driven NETosis exacerbates vascular pathology in PE. Targeting IL-8/CXCL1-CXCR1/2 signaling interrupts this pathogenic circuit, restores endothelial function, and improves maternal and fetal outcomes. These results highlight IL-8 signaling as a promising therapeutic target for PE.

Ultrasound is the most widely used imaging modality for liver evaluation because it is accessible, portable, affordable, and repeatable. Quantitative liver ultrasound should therefore be understood not as a single technology, but as a set of biomarker strategies at different stages of development. Ultrasound elastography is the most mature component of this field and has been incorporated into noninvasive pathways for fibrosis assessment. Ultrasound-based liver fat quantification is also advancing rapidly through attenuation, backscatter, and radiofrequency-based techniques. A newer area of investigation involves the evaluation of viscoelasticity and inflammatory activity using shear-wave dispersion and related biomarkers, particularly in metabolic dysfunction-associated steatotic liver disease. The future value of quantitative liver ultrasound will depend on integrating multiple parameters to support clinical triage, monitoring, and, increasingly, treatment decision-making.

BACKGROUND:Patients with acute myeloid leukemia (AML) are still at risk of relapse after consolidation therapy with cytarabine. Therefore, early identification and intervention of patients at high risk of relapse is crucial. METHODS:This single-center retrospective study analyzed the clinical data of 355 patients with AML (non-APL) who received cytarabine consolidation therapy. Key factors affecting relapse were identified by least absolute shrinkage and selection operator regression, and a predictive model for relapse after cytarabine consolidation therapy was constructed and internally validated. RESULTS:The study showed that age ≥50 years, female, DNMT3A mutation, TP53 mutation, IDH1 mutation, CBF-AML, white blood cell (WBC) ≥30 × 109/L, 2 courses of induction therapy, 1-2 courses of cytarabine consolidation therapy and cumulative dose of cytarabine <36 g were significantly correlated with relapse after cytarabine consolidation therapy in AML patients. Constructing a nomogram using the above factors and validating it with receiver operating characteristic, calibration curves showed that it has good discrimination and prediction. Patients were categorized into high-risk and low-risk groups based on the median risk score of the model, and there were significant differences in overall survival and event-free survival between the two groups. CONCLUSION:Predictive models based on age, sex, DNMT3A, TP53, IDH1, CBF-AML, WBC count, induction therapy course, cytarabine consolidation course, and cumulative dose can effectively assess the relapse risk after receiving cytarabine consolidation therapy in AML patients and provide a reference for clinical decision-making.

OBJECTIVES:Hypervirulent carbapenem-resistant Klebsiella pneumoniae (hv-CRKP) exhibiting cross-resistance to multiple antimicrobial agents represents a formidable therapeutic challenge. Therefore, elucidating the mechanisms driving antimicrobial resistance is crucial. METHODS:A ceftazidime-avibactam-resistant K. pneumoniae isolate was recovered and subjected to antimicrobial susceptibility testing and whole-genome sequencing. Resistance mechanisms were investigated through gene cloning and enzymatic kinetic assays. Public NCBI databases were interrogated to determine the prevalence of multicopy blaKPC genes. Growth curves and plasmid stability assays were performed to evaluate the fitness costs. RESULTS:An hv-CRKP isolate harboring two IS26-mediated copies of blaKPC-14 was identified following prolonged ceftazidime-avibactam therapy. The strain displayed high-level resistance to ceftazidime-avibactam, cefiderocol, and aztreonam-avibactam without appreciable fitness cost. Throughout treatment, the critically ill patient exhibited a persistently elevated estimated glomerular filtration rate. Comparative plasmid analysis demonstrated that IS26-mediated tandem duplication was widespread among plasmids carrying multiple copies of blaKPC. CONCLUSIONS:Selective pressure from prolonged ceftazidime-avibactam therapy, with potential contributions from host-related factors affecting antimicrobial exposure, could facilitate the emergence of blaKPC variants and consequently promote cross-resistance. Dual-copy blaKPC substantially increased resistance levels without imposing a significant fitness burden. These findings underscore the value of timely genomic surveillance and antimicrobial susceptibility testing for optimizing antimicrobial therapy.

Environmental metal exposure has been increasingly recognized as a potential risk factor for chronic kidney disease (CKD), yet evidence regarding nonlinear dose-response relationships and mixed-metal exposure effects remains limited. We conducted a nested case-control study within the Beijing Health Cohort, including 429 incident CKD cases and 821 matched controls. Serum concentrations of 15 metals were measured, and conditional logistic regression, restricted cubic spline (RCS), least absolute shrinkage and selection operator (LASSO), and Bayesian kernel machine regression (BKMR) models were applied to evaluate individual, nonlinear, and mixture effects of metal exposure on CKD risk. Higher serum Cu concentrations were significantly associated with increased CKD risk (OR = 7.27, 95% CI: 3.62–14.59), whereas Se (OR = 0.44, 95% CI: 0.24–0.80) and Co (OR = 0.82, 95% CI: 0.77–0.87) were inversely associated with CKD risk. Quartile analyses additionally suggested positive associations for Cd, Ba, and Cr. Significant nonlinear dose–response relationships were observed for Zn, Al, Mn, Se, Ba, Cd, and Pb. LASSO identified Cu, Se, Co, Zn, and Ba as important contributors to CKD risk, while BKMR analyses indicated a positive overall effect of mixed-metal exposure and potential inter-metal interactions. These findings suggest that metal exposure may contribute to CKD development through complex nonlinear and mixture-related mechanisms and highlight the importance of considering metal mixtures in environmental risk assessment and CKD prevention.

We aimed to develop a radiomics-clinical nomogram to predict the therapeutic effect of PC pneumonia. A total of 255 PC pneumonia patients (165 cases with good therapeutic effect and 90 cases with poor therapeutic effect) were retrospectively enrolled from two centers. 190 PC pneumonia patients from Center 1 were randomly divided into a training group (133 cases) and an internal validation group (57 cases) at a ratio of 7:3. The data from Center 2 were used as the external validation group (65 cases). The radiomics features selected from unenhanced CT were input into multiple machine learning models. The optimal model was obtained through receiver operating characteristic (ROC) analysis to construct the radiomics and clinical models. Combined them to build a nomogram for clinical application. These models were evaluated by ROC analysis, calibration curve, and decision curve analysis (DCA). The support vector machine was the best classifier with the highest area under the curve (AUC) of 0.848 in the internal validation group. Although the performance of the nomogram was similar to that of the radiomics model (AUC: 0.886 vs. 0.864; Delong test: P = 0.632), they were significantly higher than that of the clinical model (AUC: 0.886 vs. 0.730, 0.864 vs. 0.730; Delong test, P < 0.05) in the external validation group. The calibration curves and DCAs of the nomogram and radiomics models confirmed their superior predictive agreements and clinical utilities. Radiomics model and nomogram could noninvasively predict the therapeutic effect of PC pneumonia, which was beneficial for precise intervention.

Xing Gong,1,&ast; Li Wei,1,&ast; Yunshu Xu,2 Changdi Xu,3 Yi Zhang,1 Fengxia Sun,1 Man Tian,3 Liya Wang,4 Hengxue Wang,5 Ming Ge,1 Feng Liu,3 Lilin Xiong,1 Wei Pan61Department of Environment Health Promotion, Nanjing Municipal Center for Disease Control and Prevention, Nanjing, People’s Republic of China; 2Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, People’s Republic of China; 3Department of Respiratory Medicine, Children’s Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 4Department of Physical and Chemical Inspection, Nanjing Municipal Center for Disease Control and Prevention, Nanjing, People’s Republic of China; 5Department of Acute Infectious Diseases Control and Prevention, Nanjing Municipal Center for Disease Control and Prevention, Nanjing, People’s Republic of China; 6Department of Clinical Laboratory, Children’s Hospital of Nanjing Medical University, Nanjing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Lilin Xiong, Department of Environment Health Promotion, Nanjing Municipal Center for Disease Control and Prevention, Nanjing, People’s Republic of China, Email hzxionglilin@163.com Wei Pan, Department of Clinical Laboratory, Children’s Hospital of Nanjing Medical University, Nanjing, People’s Republic of China, Email panwei303303@sina.comPurpose: Extreme cold exposure may trigger respiratory morbidity in children, but whether concurrent ambient air pollution modifies cold-related risks of pediatric asthma remains unclear. This study evaluated associations between extreme cold events and pediatric asthma outpatient visits and assessed whether these associations differed according to ambient pollutant concentrations.Patients and methods: We conducted a time-series study of 62,167 pediatric asthma outpatient visits among children diagnosed with asthma at a tertiary children’s hospital in Nanjing, China, from 2019 to 2024. Daily asthma visits were linked with meteorological variables and ambient concentrations of PM10, PM2.5, NO2, SO2, CO, and O3. Extreme cold events were defined as ≥ 2, ≥ 3, or ≥ 4 consecutive days with daily mean temperature ≤ 4.5°C (the 10th percentile of the study-period distribution). Quasi-Poisson distributed-lag non-linear models were used to estimate cumulative relative risks over lag 0– 14 days. Pollutant-related effect modification was evaluated using stratified analyses and interaction measures.Results: Overall, extreme cold events were not significantly associated with pediatric asthma outpatient visits. However, stronger associations were observed during periods with higher CO and SO2 concentrations. For lag 0– 14 days, cumulative relative risks for ≥ 2-day cold events were 2.60 (95% CI: 1.13– 5.95) in the higher-CO stratum and 3.41 (95% CI: 1.15– 10.11) in the higher-SO2 stratum. Similar patterns were observed for longer cold-event definitions. Multiplicative interactions were identified for both CO and SO2, whereas additive interaction evidence was clearer for CO. Other pollutants showed less consistent modification patterns.Conclusion: The association between extreme cold events and pediatric asthma outpatient visits may depend on concurrent gaseous pollutant conditions. Relatively elevated CO and SO2 may help identify cold-event periods with greater pediatric asthma outpatient burden.Keywords: pediatric asthma outpatient visits, extreme cold events, ambient air pollutants, distributed-lag non-linear models, effect modification

Bloodstream infections caused by multidrug-resistant bacteria result in a high mortality rate. Little research has been carried out to analyze the clinical effects of laboratory-based rapid identification and susceptibility tests of the types of organisms responsible for these infections. We previously developed an ammonium chloride-based in-house workflow for direct pathogen identification and susceptibility testing from positive blood cultures, eliminating the need for subsequent overnight subculturing. In this study, we carried out a prospective single-center cohort study evaluating the outcomes associated with in-house rapid detection of carbapenem-resistant bacterial strains or methicillin-resistant Staphylococcus aureus directly from positive blood cultures. From 2018 to 2023, 584 patients with clinically significant monomicrobial bloodstream infections due to these strains were enrolled. Among the 584 patients, 412 were included in the final analysis, with 182 in the in-house group and 230 in the conventional group. The median time from positive blood culture to identification and susceptibility results was significantly shorter for the in-house rapid process than for the conventional process (P < 0.01). A decrease in laboratory report time improved the time to effective antibiotic therapy and increased the adjustment rate of effective antibiotics from 56.09% (129/230) to 70.33% (128/182) (P < 0.01). The 28-day all-cause mortality after occurrence of a bloodstream infection markedly decreased from 19.57% (45/230) to 9.34% (17/182) (P = 0.005). Our results indicate that the in-house rapid process improves the time to effective antibiotic therapy and significantly decreases patient mortality. This information should encourage the implementation of our in-house rapid diagnostic process for bloodstream infections. IMPORTANCE:In this study, we included only patients whose bloodstream infection was caused by carbapenem-resistant bacterial strains or methicillin-resistant Staphylococcus aureus, avoiding the interference of empirical treatment for sensitive strains and allowing a clearer evaluation of the effects of laboratory-based rapid workflows. Moreover, this study covered all bloodstream infection cases from 1 January 2018 to 30 June 2023, with patients randomly grouped to minimize confounding factors. The results of this study demonstrated that this laboratory-based rapid workflow significantly shortened laboratory reporting times, which led to improvements in the rate and timing of adjustment of effective antibiotic therapy and a significant decrease in the mortality of patients with bloodstream infections. To the best of our knowledge, few studies have employed such a procedure to analyze clinical effects. Therefore, we believe that our research findings will encourage the implementation of laboratory-based rapid diagnosis for bloodstream infections.

Nanozyme therapeutics received significant attention as an emerging nanomaterial for osteoarthritis (OA) treatment. However, the passive diffusion mode of nanozymes resulted in poor permeability within cartilage inflammatory lesions. Meanwhile, excessive reactive oxygen species (ROS) at inflammatory sites cause mitochondrial dysfunction, severely impairing the efficacy of cartilage repair. Here, we construct a mitochondria-targeted dual-source-driven snowman-like Janus nanomotor (PMO@PEG-COOH-MnO2@CDs@DMSN-SS31, PPMCD-SS31 Janus NMs) for synergistic cascade catalysis of superoxide dismutase (SOD-like) and catalase (CAT-like) enhanced by photothermal effects to efficiently treat OA. Such nanomotor can achieve self-thermophoresis motion under near-infrared light irradiation and SS31 targeting, effectively enhancing penetration, prolonging nanozyme retention time, accelerating carbon dot and Mn2+ release, and restoring mitochondrial membrane potential to improve cartilage repair efficacy. Furthermore, the nanomotor can exhibit highly efficient CAT-like activity, catalyzing the generation of oxygen (O2) from H2O2 overexpressed in the inflammatory microenvironment. This not only alleviates hypoxia within the lesion but also downregulates HIF-1 alpha expression. Upregulation of chondrogenic proteins and anti-inflammatory molecules, along with the conversion of macrophages M1 to M2, synergistically modulate the local immune microenvironment, thereby enabling effective cartilage repair. This study provides an effective new approach for utilizing dual-source-driven Janus nanomotors to enhance tissue permeability, eliminate inflammation, and repair cartilage.

Supplementary Figures of the manuscript

ABSTRACT Aims Metabolic dysfunction‐associated steatotic liver disease (MASLD) is a chronic liver disease closely associated with metabolic dysfunction. Research into a feasible, rapid, and effective assessment method is crucial for evaluating the therapeutic effects of semaglutide in MASLD. This study aims to explore the fast quantitative evaluation of the amelioration effect in MASLD during semaglutide therapy using ultrasound‐derived fat fraction (UDFF) and fat‐to‐muscle ratio (FMR). Methods This prospective study enrolled patients diagnosed with MASLD who were planned to be treated with subcutaneous injection of semaglutide. All patients underwent UDFF measurements (six acquisitions for each patient) using the Acuson Sequoia ultrasound system (Siemens Healthineers, Mountain View, USA) to assess hepatic steatosis before and 14 ± 2 weeks after treatment. Body composition (body weight, visceral fat area, percent body fat, waist circumference, waist‐to‐hip ratio, skeletal muscle mass index, and FMR) was analyzed by phase‐sensitive bio‐impedance. FMR was obtained to evaluate the ratio of fat mass to skeletal muscle mass. The Spearman correlation was performed to compare clinical, ultrasound parameters, and serum biomarkers. Results From July 2023 to June 2024, 22 patients (77.3% female) were included, with a median body mass index of 32.6 kg/m2. Semaglutide treatment resulted in a median weight loss of 7.5 kg from baseline. Most patients (19/22, 86.3%) achieved a significant weight reduction (weight loss ≥ 5%). Specifically, four patients had a weight loss of ≥ 5% and < 7%, nine patients had a weight loss of ≥ 7% and < 10%, and six patients had a weight loss of ≥ 10%. UDFF values were significantly decreased at follow‐up compared with those at baseline (9.4% vs. 21.8%; p < 0.001) and reached the highest reduction (49.4%). UDFF values were positively correlated with body composition at baseline (r = 0.33–0.54) and follow‐up (r = 0.30–0.65). The reduction of FMR (13.1%) was the second highest among body composition measures, after that of visceral fat area (17.3%). FMR values had a high correlation with visceral fat area at both baseline and follow‐up (r = 0.70 and r = 0.80, respectively, both p < 0.001). Conclusion UDFF and FMR values could be used as fast quantitative tools for assessing hepatic steatosis and myosteatosis in MASLD during semaglutide therapy.

Previously, we reported an ammonium chloride-based in-house rapid workflow for direct pathogen identification and antibiotic susceptibility testing from positive blood cultures, eliminating the need for the following overnight subculture. Here, we aimed to assess the workflow’s reliability, accuracy, and potential for integration into routine clinical practice. From 2018 to 2022, 1,686 monomicrobial positive blood cultures were processed by the rapid workflow: ammonium chloride lysis/centrifugation/wash followed by MALDI-TOF MS identification and, when indicated, same-day VITEK-2 direct antibiotic susceptibility testing. Results were compared with routine culture-based identification and antibiotic susceptibility testing. The overall agreement between in-house rapid workflow and conventional culture-based methods was 92.59

BACKGROUND:Total antioxidant capacity (TAC) is a critical biomarker for evaluating the nutritional quality of food and efficacy of pharmaceutical formulations. Colorimetric detection offers operational simplicity and visual readout, yet its sensitivity depends on efficient catalyst development. Nanozymes have emerged as stable, cost-effective alternatives to natural enzymes. Manganese dioxide (MnO2) exhibits intrinsic oxidase-like activity but suffers from poor conductivity and sluggish electron transfer kinetics. Engineering efficient MnO2-based oxidase mimics without hydrogen peroxide dependence remains a significant challenge for practical TAC sensing applications. RESULTS:This study developed a manganese vacancy-engineered MnO2 (MnO2-VMn) with significantly enhanced oxidase-like activity for H2O2-free colorimetric TAC detection. Comprehensive spectroscopic analyses (EPR, XPS, XAS) and DFT calculations confirmed successful Mn vacancy introduction, which reduced the work function from 5.11 eV to 4.30 eV and induced half-metallic behavior, thereby accelerating electron transfer to adsorbed O2 (0.42 e- vs. 0.32 e- for pristine Mn O2). The MnO2-VMn exhibited a 5-fold enhancement in substrate affinity (Km = 0.20 mM) compared to pristine MnO2 (Km = 1.00 mM). Using ascorbic acid and gallic acid as models, a dual-mode colorimetric platform integrating UV-Vis spectroscopy and smartphone-based RGB analysis was established, achieving detection limits of 0.13 μM and 3.24 μM, respectively. The sensor demonstrated excellent reproducibility (RSD = 1.46%), stability (16 days), and successful TAC quantification in vitamin C tablets, beverages, and fruit juices, correlating strongly with standard CUPRAC assays. SIGNIFICANCE:This work presents the systematic demonstration of Mn vacancy engineering to enhance the oxidase-like activity of MnO2. We conclude that cationic vacancies modulate electronic structure, reducing work function and promoting interfacial electron transfer for efficient O2 activation. This significant finding provides a new design strategy for high-performance nanozymes and establishes a reliable, field-deployable platform for antioxidant quality assessment in food and pharmaceutical products.