Nantong University

Exploring efficient electrocatalysts for the formic acid oxidation reaction (FAOR) through dehydrogenation pathway is important for the widespread use of direct formic acid fuel cells (DFAFCs). The direct pathway, reactivity...

Background POU4F3 mutations cause DFNA15, an autosomal dominant nonsyndromic hearing loss. POU4F3 encodes a transcription factor crucial for inner ear hair cell development and maintenance. Objective To identify and functionally characterize novel POU4F3 mutations in two Chinese families with late‐onset progressive hearing loss. Methods Massively parallel DNA sequencing (MPS) was performed on affected individuals from two unrelated Chinese families. Sanger sequencing validated mutations and confirmed co‐segregation. Functional analyses included protein expression analysis by Western blots and subcellular localization studies by immunofluorescence. Results We identified two novel nonsense mutations in POU4F3: c.863C > A (p.Ser288Ter) and c.172G > T (p.Glu58Ter), both co‐segregating with the hearing loss phenotype. Functional studies showed p.Ser288Ter produced a stable but mislocalized protein with impaired nuclear transport, while p.Glu58Ter resulted in a severely truncated, rapidly degraded protein. Conclusion This study expands the DFNA15 mutation spectrum and provides new insights into POU4F3‐related hearing loss pathogenesis. Our findings demonstrate that different molecular mechanisms can lead to similar DFNA15 phenotypes, supporting POU4F3 haploinsufficiency as the primary pathogenic mechanism.

Aims Spinal cord injury (SCI) disrupts tissue homeostasis, leading to persistent neuroinflammation and scar formation that severely impedes functional recovery. Current therapeutic approaches are insufficient to address these challenges. In this study, we investigated whether exogenous hydrogen sulfide (H2S) can modulate neuroinflammatory responses and remodel the injury microenvironment to promote tissue repair and restore motor function following SCI. Methods T10 crush SCI was induced in mice, followed by daily intraperitoneal administration of the H2S donor anethole trithione (ADT). Immunofluorescence staining, tissue clearing, western blotting, and behavioral assessments were performed to evaluate scar formation, vascular regeneration, neuronal survival, and motor function. Results ADT‐based H2S therapy significantly promoted wound healing, inhibited scar formation, enhanced vascular regeneration, and protected residual neurons and axons from secondary injury. Mechanistically, H2S suppressed microglial proliferation and activation, promoting their polarization toward an anti‐inflammatory phenotype and alleviating neuroinflammation. Consequently, motor function recovery was markedly improved. Conclusion H2S modulates microglial activation and mitigates neuroinflammation, establishing a permissive microenvironment for SCI repair and significantly enhancing motor function recovery. Given ADT's established clinical safety and its effective gasotransmitter properties, our findings underscore its immediate translational potential for treating SCI.

This letter proposes a novel miniaturized triple-mode (TM) dielectric waveguide resonator (DWR) for the design of bandpass filters (BPFs) with controllable transmission zeros (TZs). The TM DWR consists of two quarter-wavelength metallized non-through holes and one T-shaped half-wavelength metallized through hole on a dielectric block with silver-plated surface. The three modes employed are the quasi-TEM modes generated by the two metallized non-through holes and the TEM mode generated by the metallized through hole. The resonant frequency of each mode and the coupling between different modes can be controlled independently. The position of the TZs can be controlled by adjusting the relative location of the metallized non-through holes. Based on the TM DWR, two BPFs were designed with TZs located above and below the passband.

This article proposes a novel design method for introducing a lower transmission zero (TZ) in a ridge waveguide (WG) spoof surface plasmon polariton (SSPP) bandpass filter (BPF). Initially, the inverted T-shaped slots are etched on the central ridge to construct the SSPP and then design a wideband BPF, and the upper and lower cutoff frequencies of passband are determined by the SSPP and double gratings (DGs) on both sides of the ridge WG. To improve the rejection roll-off of the lower stopband, a pair of folded slots are etched at both ends of the ridge to form a quarter-wavelength (λ/4) resonance for introducing a TZ below the passband without increasing the size. Additionally, the stopband of the proposed SSPP filter can be extended by optimizing the DGs to suppress the higher order modes of the SSPP. To demonstrate this method, a ridge WG BPF was fabricated and measured. The measurement results indicate that the filter possesses favorable frequency selectivity and a wide stopband.

Objective To evaluate the association between the inflammatory biomarkers and the prevalence of febrile urinary tract infection (fUTI) after double-J (DJ) stent removal in pediatrics following laparoscopic pyeloplasty (LP). Methods A retrospective study was conducted in pediatrics underwent DJ stent removal following LP owing to primary ureteropelvic junction obstruction (UPJO) between September 2021 and November 2024. Baseline characteristics, preoperative data and the incidence of fUTI were documented. The inflammatory index including neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR) and systemic immune-inflammation index (SII) were calculated. The results of cultured pathogens were also identified. The univariate and multivariate logistic analysis were conducted to determine the potential risk factors of fUTI after DJ stent removal. The predictive value of potential risk factors were determined by receiver operating characteristic curve (ROC). Results Overall, 295 patients were included in the study. fUTI occurred in 22 patients (7.5%) after DJ stent removal. Patients in the fUTI group were younger (P = 0.008) and had lower body weight (P = 0.003) compared to non-fUTI group. Additionally, the fUTI group showed higher levels of platelets and neutrophils, associated with lower levels of lymphocytes. The most commonly identified pathogens were Enterococcus and Escherichia coli in fUTI patients. Multivariate logistic analysis revealed that age (OR = 0.978, 95% CI: 0.956–0.999, P = 0.047), toilet training status (OR = 0.297, 95% CI: 0.109–0.807, P = 0.017) and higher levels of PLR (OR = 1.101, 95% CI: 1.005–1.022, P = 0.002) were predictive factors for fUTI after DJ stent removal. PLR had a high predictive value with an AUC of 0.827 with the sensitivity of 90.91% and the specificity of 69.23%. Conclusion PLR is a promising predictor for diagnosing fUTI after DJ stent removal. Patients with higher levels of PLR before DJ stent removal should be closely monitored. Further well-designed and prospective cohorts are required in future to explore the cause-and-effect relationship between PLR and fUTI after DJ removal.

Purpose To evaluate the clinicoradiological significance of intrahepatic periportal hyperintensity (PHI) detected by gadoxetate-enhanced hepatobiliary phase (HBP) MRI and T2-weighted imaging (T2WI), and to assess its potential as a noninvasive imaging biomarker for clinical stratification of liver injury in patients with cirrhosis. Methods This retrospective study included 37 cirrhotic patients with intrahepatic diffuse PHI on HBP imaging, who underwent gadoxetate-enhanced MRI between October 2019 and November 2023. PHI patterns were classified into two groups based on the spatial concordance between periportal enhancement areas on HBP and periportal hyperintense areas on T2WI. The matching group (Type A, n = 21) demonstrated complete spatial overlap between the two sequences. The mismatching group, comprised Type B (n = 11), in which PHI on HBP was immediately outside of that on T2WI, and Type C (n = 5), in which PHI was present on HBP but absent on T2WI. Clinical etiologies and liver biochemical markers (ALT, AST, GGT, TBil, DBil, ALP, Alb, TP) were compared across PHI subtypes. Results Type A PHI was predominantly associated with acute liver injury (e.g., acute viral hepatitis flares, drug-induced liver injury, autoimmune hepatitis), characterized by a strong ALT-AST correlation (r = 0.95, P < 0.001) and significantly elevated levels of ALT, AST, GGT, TBil, and DBil (all P < 0.001). In contrast, Types B and C PHI were primarily linked to chronic fibrotic conditions (e.g., HBV/HCV-related cirrhosis, primary biliary cholangitis, and primary sclerosing cholangitis), showing a strong TBil-DBil correlation (r = 0.95, P < 0.001) and moderately elevated ALP and Alb levels (P = 0.027 and P = 0.017, respectively). Receiver operating characteristic (ROC) analysis identified DBil > 37.5 μmol/L as the optimal threshold for differentiating Type A from Types B/C PHI (AUC = 0.922; sensitivity = 86.7%, specificity = 100%). Notably, HBP-doughnut nodules without arterial-phase hyperenhancement (APHE) were exclusively observed in the mismatching group (Type B: 4/11; Type C: 3/5), further supporting their association with chronic fibrotic changes. Conclusion PHI phenotyping based on HBP-T2WI spatial concordance enables accurate, noninvasive differentiation between acute inflammatory and chronic fibrotic liver injury in cirrhotic patients. When integrated with the DBil threshold, this imaging-based approach provides as a robust biomarker for clinical stratification of liver injury and may facilitate individualized diagnosis and therapeutic decision-making in chronic liver disease.

Lymphoma, a clonal malignancy from lymphocytes, includes diverse subtypes requiring distinct immunohistochemical stains for accurate diagnosis. Limited biopsy specimens often restrict the use of multiple stains, complicating diagnostic workflows. Lymphomas are typically classified into B‐cell and T‐cell types, each with specific markers. This study represents the first feasibility study in deploying deep learning models for B‐ and T‐cell lymphoma classification on histopathological images. We analyzed 1510 H&E‐stained sections (750 B‐cell, 760 T‐cell) with CNN models (Xception, NASNetL, ResNet50, EfficientNet), enhanced by Convolutional Block Attention Modules (CBAMs). All models demonstrated strong classification capabilities, with EfficientNet achieving the highest accuracy at 91.5% and the best precision at 91.9%, while Xception performed the best recall at 91.5%. Furthermore, the deep learning models significantly outperformed human pathologists in classification accuracy and inference speed, processing images in milliseconds compared to the several seconds required for manual diagnosis. These findings underscore the effectiveness of advanced CNN models in improving diagnostic precision while reducing dependency on manual staining and interpretation, and the integration of AI‐driven classification can provide valuable support for pathologists.

Osteoporotic bone defect and fracture healing remain significant challenges in clinical practice. While traditional therapeutic approaches provide some regulation of bone homeostasis, they often present limitations and adverse effects. In orthopedic procedures, bone cement serves as a crucial material for stabilizing osteoporotic bone and securing implants. However, with the exception of magnesium phosphate cement, most cement variants lack substantial bone regenerative properties. Recent developments in biomaterial science have opened new avenues for enhancing bone cement functionality through innovative modifications. These advanced materials demonstrate promising capabilities in modulating the bone microenvironment through their distinct physicochemical properties. This review provides a systematic analysis of contemporary biomaterial-based modifications of bone cement, focusing on their influence on the bone healing microenvironment. The discussion begins with an examination of bone microenvironment pathology, followed by an evaluation of various biomaterial modifications and their effects on cement properties. The review then explores regulatory strategies targeting specific microenvironmental elements, including inflammatory response, oxidative stress, osteoblast-osteoclast homeostasis, vascular network formation, and osteocyte-mediated processes. The concluding section addresses current technical challenges and emerging research directions, providing insights for the development of next-generation biomaterials with enhanced functionality and therapeutic potential. Graphical Abstract

In this paper, two models of toxic phytoplankton and zooplankton with strong or weak Allee effect are investigated. It is discussed whether the equilibria are stable and Hopf bifurcation exists. When the strong Allee effect rate is high and the toxin-producing rate is less, both of two species will remain extinct. However, when the toxin-producing rate increases, the system will possess either a stable limit cycle or a coexistence equilibrium. When the weak Allee effect rate is high, the immediate toxin-producing might result in the occurrence of coexistent equilibrium or a subcritical (supercritical) Hopf bifurcation. A degenerate Hopf bifurcation is also demonstrated by numerical simulation, where the inner limit cycle is stable and the outer one is unstable. For both strong Allee effect and weak Allee effect cases, the obtained results show that immediate production of toxin can aid in the survival of both species and large toxin-producing will be detrimental to the growth of zooplankton. The findings in this research can be viewed as a complement to those with the Allee effect.

Artificial intelligence applications in oncology imaging often struggle with diagnosing rare tumors. We identify significant gaps in detecting uncommon thyroid cancer types with ultrasound, where scarce data leads to frequent misdiagnosis. Traditional augmentation strategies do not capture the unique disease variations, hindering model training and performance. To overcome this, we propose a text-driven generative method that fuses clinical insights with image generation, producing synthetic samples that realistically reflect rare subtypes. In rigorous evaluations, our approach achieves substantial gains in diagnostic metrics, surpasses existing methods in authenticity and diversity measures, and generalizes effectively to other private and public datasets with various rare cancers. In this work, we demonstrate that text-guided image augmentation substantially enhances model accuracy and robustness for rare tumor detection, offering a promising avenue for more reliable and widespread clinical adoption.

Recent studies indicate that Secretogranin V (SCG5) is aberrantly expressed in various cancers and may be linked to tumor progression and prognosis. This study aims to evaluate the potential of SCG5 as a prognostic biomarker for non-small cell lung cancer (NSCLC). We employed a combination of bioinformatics analysis, Western blotting, and immunofluorescence techniques to investigate the role of SCG5 in NSCLC. A comprehensive analysis of TCGA and GEO pan-cancer datasets revealed a consistent upregulation of SCG5 across multiple cancer types. In NSCLC, SCG5 expression was significantly higher in tumor tissues compared to normal lung tissues (p < 0.001). Kaplan-Meier survival analysis demonstrated that patients with elevated SCG5 expression exhibited lower overall survival rates, suggesting a strong association with poor prognosis. Univariate and multivariate COX regression analyses, conducted on both TCGA cases and our collected patient data, confirmed SCG5 as an independent prognostic factor for NSCLC. Furthermore, immune infiltration analysis indicated a significant correlation between SCG5 expression and various immune cell subpopulations, underscoring its potential role as a biomarker for adverse outcomes. Western blot analysis further validated the elevated levels of SCG5 in NSCLC tissues and cell lines compared to their normal counterparts. Based on our findings, we hypothesize that SCG5 may serve as a valuable biomarker for predicting the prognosis of non-small cell lung cancer, thereby guiding future research in the fields of diagnosis, progression, therapy, and prognosis of NSCLC.

Objective: The aim of this research was to examine the feasibility and effects of the “Rebuilding Myself” intervention to enhance adaptability of cancer patients to return to work. Methods: A randomized controlled trial with a two-arm, single-blind design was employed. The control group received usual care, whereas the intervention group received “Rebuilding Myself” interventions. The effects were evaluated before the intervention, mid-intervention, and post-intervention. The outcomes were the adaptability to return to work, self-efficacy of returning to work, mental resilience, quality of life, and work ability. Results: The results showed a recruitment rate of 73.17%, a retention rate of 80%. Statistically significant differences were found between the two groups in cancer patients’ adaptability to return to work, self-efficacy to return to work, mental resilience, and the dimension of bodily function, emotional function, fatigue, insomnia, and general health of quality of life. Conclusion: “Rebuilding Myself” intervention was proven to be feasible and can initially improve cancer patients’ adaptability to return to work. The intervention will help provide a new direction for clinicians and cancer patients to return to work. DESCRIPTORS Cancer; Return to Work; Compliance; Nursing; Feasibility Studies

A fast, simple and effective method was developed and validated for determination of 11 fungicides using magnetic solid-phase extraction with NH2-Fe3O4@GO (graphene oxide) combined with gas chromatography–triple quadrupole mass spectrometry (GC–MS/MS). To carry out the extraction of the fungicides from samples, NH2-Fe3O4@GO nanocomposites were synthesized and characterized by scanning electron microscopy, Fourier transform infrared spectroscopy and X-ray diffraction. The target analytes were extracted on NH2-Fe3O4@GO and then eluted by ethyl acetate and acetonitrile (1:1 v/v). Finally, the extraction solvent concentrated by nitrogen blowing was analyzed by GC–MS/MS, which demonstrated good linearity between 0.05 and 5.0 mg L−1. The limits of detection (signal-to-noise ratio = 3) and the limits of quantification (signal-to-noise ratio = 10) for the 11 fungicides ranged from 1.0 to 3.5 and 3.0 to 10.5 μg kg−1, respectively. The accuracy and precision of the proposed method were evaluated by measuring tagged samples; the recoveries and relative standard deviations ranged from 75.3% to 103.9% and 2.19% to 4.68%, respectively. The utility of the adsorbent was demonstrated to determine trace fungicides in fresh fruit juice samples.

Thermoelectric (TE) materials have garnered considerable attention because they can be used to directly convert heat into electricity. Although organic TE materials have advantages of no or low toxicity, low cost, high mechanical flexibility, and solution processability, their TE properties, particularly the Seebeck coefficient, are saliently inferior to the inorganic counterparts. Here, the significant enhancement is reported in the Seebeck coefficient, and thus the overall TE properties of poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) films through a treatment with 4‐(1,3‐dimethyl‐2,3‐dihydro‐1H‐benzoimidazol‐2‐yl)phenyl)dimethylamine (N‐DMBI), which is a popular organic donor. The PEDOT:PSS films treated sequentially with H2SO4, NaOH, dimethyl sulfoxide (DMSO), and 4‐(1,3‐dimethyl‐2,3‐dihydro‐1H‐benzoimidazol‐2‐yl)phenyl)dimethylamine (N‐DMBI) can exhibit a high Seebeck coefficient of 64.1 µV K⁻¹ and electrical conductivity of 1864 S cm⁻¹, and the corresponding power factor is 765.1 µW m⁻¹ K⁻², the highest for solid polymer films. N‐DMBI can partially dedope PEDOT:PSS. In addition, it can have π–π overlapping with the conjugated PEDOT chains, which induces the splitting of the lower polaron level, thereby lifting the Fermi level and enhancing the Seebeck coefficient of PEDOT:PSS.

Accurate measurement of human insulin is critical for proper diagnosis, monitoring, and treatment of diabetes. But the insulin results of clinical immunoassay are inconsistent mainly due to antibody cross-reactivity. To standardize and ensure the accuracy of clinical assays, reference measurement procedures (RMPs) with higher metrological order are required. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC–MS/MS) for quantification of human insulin in serum as a candidate reference measurement procedure (cRMP) was developed and validated. Insulin was enriched from human serum by insulin antibodies immobilized on magnetic beads. The eluent was analyzed by ID-LC–MS/MS. The cRMP separated human insulin from potentially interfering compounds and enabled measurement over a range of 0.05–40 ng/g, with no matrix effects and carryover. The limit of detection (LOD) and the limit of quantitation (LOQ) in serum were 24.6 pg/g and 48.8 pg/g, respectively. Imprecision (intra-assay and inter-assay) was <2.77% at 0.436, 2.003, and 11.449 ng/g. Recoveries ranged from 99.5% to 101.7% at three spiked levels. Extraction recovery was measured at 85% or higher. Insulin analogues caused no interference for the determination of endogenous insulin. Expanded measurement uncertainty of target value-assigned samples was ≤3.5%. The cRMP was applied to measure human insulin in serum and was compared with two immunoassays using 46 serum samples. Also, a discrepancy of three candidate reference materials for the calibration of cRMP was discussed.

Aims While MRI serves as a tool for assessing the severity of lumbar disc herniation (LDH), it has been observed that imaging diagnoses do not always align with clinical symptoms in nearly half of patients. The absence of dependable prognostic biomarkers impedes the early and accurate diagnosis of LDH, which is critical for the development of further treatment approaches. Thus, the aim of this study was to elucidate the molecular mechanisms that determine pain and LDH severity. Methods We conducted a pilot study with 55 patients, employing transcriptomic and metabolomic analyses on blood samples to identify potential biomarkers. A gene-metabolite interaction approach helped in identifying the pivotal pathway linked to disease severity. Moreover, a machine-learning model was designed to differentiate between patients based on the intensity of pain. Results Cholinergic-related glycerophospholipid metabolism emerged as the predominant enriched pathway in the severe symptom group via gene-metabolite interaction network analysis. Among various models, the gradient boosting machines (GBM) model stood out, achieving a commendable area under the curve (AUC) of 0.875 in distinguishing between the severe and mild symptom groups using combined RNA and metabolomics data. Conclusion Integrated molecular profiling of blood biomarkers has highlighted a novel determining pathway for LDH severity. This machine-learning approach can serve as a valuable predictive tool when MRI findings are inconclusive. Future research will focus on validating these biomarkers and exploring their potential for personalized medicine approaches. Cite this article: Bone Joint Res 2025;14(5):434–447.