ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Owing to its inherent advantages—such as being non-destructive, rapid, highly molecule-specific, and minimally interfered with by moisture—Raman spectroscopy has been widely adopted in the fields of skin barrier function assessment, monitoring the transdermal penetration of active cosmetic ingredients, and the identification and quality control of cosmetic products. Despite these strengths, the practical application of this technique faces a significant bottleneck: the strong fluorescence background generated by endogenous skin components and exogenous cosmetic additives. Endogenous skin substances, such as structural proteins (e.g., collagen and elastin), metabolic coenzymes (e.g., nicotinamide adenine dinucleotide), and pigments (e.g., melanin), together with exogenous cosmetic constituents like organic colorants, chemical sunscreens, and fragrances, often possess strong absorption and emission characteristics. When excited by lasers, these components produce a fluorescence background that can be 10⁶ to 10⁸ times stronger than the Raman scattering signals, effectively masking the inherently weak vibrational fingerprint information. In recent years, driven by the rapid development of optoelectronic hardware and artificial intelligence algorithms, fluorescence suppression strategies have evolved from isolated, single-method approaches into comprehensive, multi-level synergistic systems. These systems are categorized into three distinct tiers: sample preparation, signal acquisition, and data processing. At the sample preparation level, techniques such as photobleaching and surface-enhanced Raman spectroscopy (SERS) are employed to eliminate or bypass the generation of fluorescence at the source. At the signal acquisition level, instrumental improvements—including the use of long-wavelength near-infrared excitation (typically 785 nm or 1 064 nm), confocal spatial filtering, and shifted excitation Raman difference spectroscopy (SERDS)— are utilized to physically isolate Raman signals from the fluorescence background. Furthermore, at the data processing level, numerical baseline correction methods such as polynomial fitting, penalized least squares (e.g., airPLS, arPLS), wavelet transform, and derivative algorithms are increasingly integrated into the analytical pipeline to extract Raman spectral features from mixed signals without increasing hardware costs or acquisition time. This review provides a systematic categorization and critical evaluation of these fluorescence suppression methods, detailing their underlying principles, technical advantages, and inherent limitations in diverse experimental setups. By focusing on critical application scenarios—including skin barrier assessment, percutaneous absorption monitoring, the routine quality control of cosmetics, and the emerging field of portable on-site detection—this paper explores the current state of technique selection and optimization. Finally, the article discusses future development trends, emphasizing the necessity of constructing adaptive, tiered suppression strategies, developing intelligent and automated data processing algorithms, and promoting the integration of portable, multi-modal diagnostic devices. The objective of this review is to provide a comprehensive technical reference to facilitate the transition of Raman spectroscopy from a specialized laboratory tool into a routine, robust analytical platform for advancements in skin science and cosmetic research.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

OBJECTIVES: To study the effect of prophylactic phenytoin (PHT) or levetiracetam (LEV) on busulfan (BU) blood concentration in children undergoing hematopoietic stem cell transplantation. METHODS: Pediatric patients conditioned with BU plus cyclophosphamide and fludarabine at the First People's Hospital of Chenzhou from September 2023 to February 2025 were retrospectively included. Patients were grouped by prophylactic antiepileptic regimen into PHT (n=24) and LEV (n=26). BU blood concentrations at the end of infusion (0 hour) and at 1, 2, and 4 hours post-infusion were compared between groups. RESULTS: At 0 hour post-infusion, BU blood concentrations did not differ significantly between groups (P>0.05). At 1, 2, and 4 hours post-infusion, BU blood concentrations were higher in the LEV group than in the PHT group (P<0.05). The area under the concentration-time curve from 0 to ∞ (AUC_0-∞) was greater in the LEV group (P<0.001), and the attainment rate of AUC_0-∞ was higher in the LEV group than in the PHT group (73% vs 21%, P<0.001). No significant differences were observed between groups in time to hematopoietic engraftment or in the incidence of BU-related adverse drug reactions (P>0.05). CONCLUSIONS Compared with PHT, LEV prophylaxis is associated with higher BU blood concentration and a higher AUC_0-∞ attainment rate. There is no observed difference in BU efficacy or safety between PHT and LEV.
Humans ; Levetiracetam/therapeutic use* ; Busulfan/pharmacokinetics* ; Hematopoietic Stem Cell Transplantation ; Male ; Female ; Child ; Child, Preschool ; Phenytoin/pharmacology* ; Infant ; Retrospective Studies ; Anticonvulsants/pharmacology* ; Adolescent

OBJECTIVE: To explore the safety and efficacy of high-dose etoposide (VP-16) combined with recombinant human granulocyte colony-stimulating factor (rhG-CSF) as salvage mobilization for peripheral blood stem cells (PBSC) in newly diagnosed multiple myeloma (NDMM) patients. METHODS: From April 2021 to May 2023, eight NDMM patients who had failed to yield sufficient PBSC during initial mobilization with high-dose cyclophosphamide (CTX) combined with rhG-CSF underwent salvage mobilization with 1.2 g/m2 etoposide combined with rhG-CSF 10 μg/(kg·d). The effects and adverse reactions of initial mobilization and salvage mobilization were analyzed. RESULTS: For salvage mobilization and initial mobilization, the numbers of PBSC collections were 16 and 18, respectively. The mean value of total collected CD34⁺ cells were (11.90±5.75)×10⁶/kg and (1.67±0.75)×10⁶/kg (P =0.0010) in salvage mobilization group and initial mobilization group, respectively. The proportion of patients with a total collection of CD34⁺ cell count≥2×10⁶/kg were 100% and 37.5% (P =0.0625), and the proportion of patients with a total collection of CD34⁺ cell count≥5×10⁶/kg were 87.5% and 0% (P =0.0156) in salvage mobilization group and initial mobilization group, respectively. For five patients who underwent high-dose CTX initial mobilization but had a total CD34⁺ cell count < 2×10⁶/kg, successful collection was achieved through salvage mobilization with high-dose VP-16. Salvage mobilization with high-dose VP-16 was scheduled 2-3 weeks after failure of CTX mobilization. Adverse reactions of high-dose VP-16 mobilization did not increase compared to the initial mobilization with high-dose CTX. CONCLUSION As a salvage mobilization regimen, VP-16 1.2 g/m² combined with rhG-CSF is safe and highly effective in NDMM patients who failed to initial mobilization with high-dose CTX combined with rhG-CSF.
Humans ; Multiple Myeloma/therapy* ; Etoposide/therapeutic use* ; Hematopoietic Stem Cell Mobilization/methods* ; Cyclophosphamide/therapeutic use* ; Granulocyte Colony-Stimulating Factor ; Salvage Therapy ; Peripheral Blood Stem Cells ; Male ; Middle Aged ; Female ; Peripheral Blood Stem Cell Transplantation

OBJECTIVE: To investigate possible associations between physical function assessment scales, such as Short Physical Performance Battery (SPPB) and Berg Balance Scale (BBS), with all-cause mortality in acute decompensated heart failure (ADHF) patients. METHODS: A total of 108 ADHF patients were analyzed from October 2020 to October 2022, and followed up to May 2023. The association between baseline clinical characteristics and all-cause mortality was analyzed by univariate Cox regression analysis, while for SPPB and BBS, univariate Cox regression analysis was followed by receiver operating characteristic curves, in which the area under the curve represented their predictive accuracy for all-cause mortality. Incremental predictive values for both physical function assessments were measured by calculating net reclassification index and integrated discrimination improvement scores. Optimal cut-off value for BBS was then identified using restricted cubic spline plots, and survival differences below and above that cut-off were compared using Kaplan-Meier survival curves and the log-rank test. The clinical utility of BBS was measured using decision curve analysis. RESULTS: For baseline characteristics, age, female, blood urea nitrogen, as well as statins, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, or angiotensin receptor-neprilysin inhibitors, were predictive for all-cause mortality for ADHF patients. With respect to SPPB and BBS, higher scores were associated with lower all-cause mortality rates for both assessments; similar area under the curves were measured for both (0.774 for SPPB and 0.776 for BBS). Furthermore, BBS ≤ 36.5 was associated with significantly higher mortality, which was still applicable even adjusting for confounding factors; BBS was also found to have great clinical utility under decision curve analysis. CONCLUSIONS BBS or SPPB could be used as tools to assess physical function in ageing ADHF patients, as well as prognosticate on all-cause mortality. Moreover, prioritizing the improvement of balance capabilities of ADHF patients in cardiac rehabilitation regimens could aid in lowering mortality risk.

OBJECTIVE: To investigate the effects of astragaloside IV (AS-IV) on podocyte injury of diabetic nephropathy (DN) and reveal its potential mechanism. METHODS: In in vitro experiment, podocytes were divided into 4 groups, normal, high glucose (HG), inositol-requiring enzyme 1 (IRE-1) α activator (HG+thapsigargin 1 µmol/L), and IRE-1α inhibitor (HG+STF-083010, 20 µmol/L) groups. Additionally, podocytes were divided into 4 groups, including normal, HG, AS-IV (HG+AS-IV 20 µmol/L), and IRE-1α inhibitor (HG+STF-083010, 20 µmol/L) groups, respectively. After 24 h treatment, the morphology of podocytes and endoplasmic reticulum (ER) was observed by electron microscopy. The expressions of glucose-regulated protein 78 (GRP78) and IRE-1α were detected by cellular immunofluorescence. In in vivo experiment, DN rat model was established via a consecutive 3-day intraperitoneal streptozotocin (STZ) injections. A total of 40 rats were assigned into the normal, DN, AS-IV [AS-IV 40 mg/(kg·d)], and IRE-1α inhibitor [STF-083010, 10 mg/(kg·d)] groups (n=10), respectively. The general condition, 24-h urine volume, random blood glucose, urinary protein excretion rate (UAER), urea nitrogen (BUN), and serum creatinine (SCr) levels of rats were measured after 8 weeks of intervention. Pathological changes in the renal tissue were observed by hematoxylin and eosin (HE) staining. Quantitative reverse transcription-polymerase chain reaction (RT-PCR) and Western blot were used to detect the expressions of GRP78, IRE-1α, nuclear factor kappa Bp65 (NF-κBp65), interleukin (IL)-1β, NLR family pyrin domain containing 3 (NLRP3), caspase-1, gasdermin D-N (GSDMD-N), and nephrin at the mRNA and protein levels in vivo and in vitro, respectively. RESULTS: Cytoplasmic vacuolation and ER swelling were observed in the HG and IRE-1α activator groups. Podocyte morphology and ER expansion were improved in AS-IV and IRE-1α inhibitor groups compared with HG group. Cellular immunofluorescence showed that compared with the normal group, the fluorescence intensity of GRP78 and IRE-1α in the HG and IRE-1α activator groups were significantly increased whereas decreased in AS-IV and IRE-1α inhibitor groups (P<0.05). Compared with the normal group, the mRNA and protein expressions of GRP78, IRE-1α, NF-κ Bp65, IL-1β, NLRP3, caspase-1 and GSDMD-N in the HG group was increased (P<0.05). Compared with HG group, the expression of above indices was decreased in the AS-IV and IRE-1α inhibitor groups, and the expression in the IRE-1α activator group was increased (P<0.05). The expression of nephrin was decreased in the HG group, and increased in AS-IV and IRE-1α inhibitor groups (P<0.05). The in vivo experiment results revealed that compared to the normal group, the levels of blood glucose, triglyceride, total cholesterol, BUN, blood creatinine and urinary protein in the DN group were higher (P<0.05). Compared with DN group, the above indices in AS-IV and IRE-1α inhibitor groups were decreased (P<0.05). HE staining revealed glomerular hypertrophy, mesangial widening and mesangial cell proliferation in the renal tissue of the DN group. Compared with the DN group, the above pathological changes in renal tissue of AS-IV and IRE-1α inhibitor groups were alleviated. Quantitative RT-PCR and Western blot results of GRP78, IRE-1α, NF-κ Bp65, IL-1β, NLRP3, caspase-1 and GSDMD-N were consistent with immunofluorescence analysis. CONCLUSION AS-IV could reduce ERS and inflammation, improve podocyte pyroptosis, thus exerting a podocyte-protective effect in DN, through regulating IRE-1α/NF-κ B/NLRP3 signaling pathway.
Podocytes/metabolism* ; Animals ; Diabetic Nephropathies/metabolism* ; Saponins/therapeutic use* ; Triterpenes/therapeutic use* ; Signal Transduction/drug effects* ; NF-kappa B/metabolism* ; Protein Serine-Threonine Kinases/metabolism* ; Male ; Rats, Sprague-Dawley ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Endoribonucleases/metabolism* ; Endoplasmic Reticulum Chaperone BiP ; Rats ; Diabetes Mellitus, Experimental/complications* ; Endoplasmic Reticulum/metabolism* ; Multienzyme Complexes