Objective To explore the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. Methods A cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. Results The prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. Conclusion Depression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

Hemoglobin (Hb) concentration is a key clinical biomarker for diagnosing and managing anemia, ischemic stroke, perioperative blood loss, and chronic diseases such as renal failure. Traditional venous blood sampling remains the gold standard due to its high accuracy, but its invasive nature limits frequent testing, real time monitoring, and large scale screening. This has driven growing interest in non-invasive Hb detection technologies over the past decade. Among these, optical methods are the most promising because of their safety, potential for continuous monitoring, and compatibility with portable or wearable devices. This paper systematically reviews major advances in optical non invasive Hb detection from the last ten years. We focus on near-infrared spectroscopy branches—photoplethysmography (PPG) and dynamic spectrum (DS)—and also cover color analysis/RGB imaging, Raman spectroscopy, and photoacoustic spectroscopy. For each technology, we explain its detection principles, analyze advantages and limitations, and summarize optimization strategies reported in recent literature. PPG, based on pulsatile blood volume changes, underpins many commercial continuous monitors. However, its accuracy is constrained by motion artifacts, individual physiological variations (e.g., skin tone, tissue thickness), and low AC signal to noise ratio. In contrast, DS—an advanced derivative of PPG—uses a differential principle to extract absorbance changes between systolic and diastolic peaks. This theoretically eliminates interference from static tissues (skin, bone, venous blood) and common mode noise (e.g., ambient light), positioning DS as a more robust framework for high precision Hb quantification. Beyond spectral methods, color analysis/RGB imaging offers a hardware minimalist approach. By analyzing images of vascular rich, thin tissues (e.g., conjunctiva, nail beds, palms), it enables Hb estimation using smartphone cameras. Recent advances have shifted from manual RGB feature extraction to deep learning models and spectral super resolution that reconstruct hyperspectral data from RGB inputs, significantly improving screening accuracy. Our academic perspective emphasizes critical and integrative analysis. We highlight persistent challenges that hinder clinical translation: profound individual biological variability (skin optics, microvascular architecture), sensitivity to measurement conditions (pressure, ambient light), and a lack of standardized validation protocols and multi center trials. A central thesis is that no single optical method is universally superior; each involves trade offs between accuracy, complexity, cost, and practicality. Looking forward, we posit that the next performance leap will come from multimodal information fusion—combining PPG, electrocardiogram (ECG), bioimpedance, or different optical modalities to compensate for individual differences and environmental noise. AI and deep learning are essential not only for image analysis but also for automated, end to end feature extraction from complex waveforms like PPG sequences. Advancing hardware (tunable lasers, quantum dot LEDs, novel sensor designs) is crucial to improve signal fidelity and portability. Finally, we advocate for clinical scenario specific optimization and rigorous standardized evaluation frameworks to gain regulatory approval (e.g., FDA, NMPA) and achieve widespread clinical acceptance. In conclusion, this review synthesizes a decade of progress. Optical non-invasive Hb detection has evolved from proof of concept studies to emerging products and validated screening tools, but the journey toward reliable, clinic ready quantitative devices continues. The convergence of smarter algorithms, fused sensing modalities, and focused clinical validation offers the most promising path to transform this potential into routine medical practice, ultimately enabling personalized, continuous, and accessible hematological management.

Inflammatory diseases (IDs) are a general term of diseases characterized by chronic inflammation as the primary pathogenetic mechanism, which seriously affect the quality of patient′s life and cause significant social and medical burden. Current drugs for IDs include nonsteroidal anti-inflammatory drugs, corticosteroids, immunomodulators, biologics, and antioxidants, but these drugs may cause gastrointestinal side effects, induce or worsen infections, and cause non-response or intolerance. Given the outstanding performance of metal polyphenol network (MPN) in the fields of drug delivery, biomedical imaging, and catalytic therapy, its application in the diagnosis and treatment of IDs has attracted much attention and significant progress has been made. In this paper, we first provide an overview of the types of IDs and their generating mechanisms, then sort out and summarize the different forms of MPN in recent years, and finally discuss in detail the characteristics of MPN and their latest research progress in the diagnosis and treatment of IDs. This research may provide useful references for scientific research and clinical practice in the related fields.

Clinical practice guidelines represent the best recommendations for patient care. They are developed through systematically reviewing currently available clinical evidence and weighing the relative benefits and risks of various interventions. However, clinical practice guidelines have to go through a long translation cycle from development and revision to clinical promotion and application, facing problems such as scattered distribution, high duplication rate, and low actual utilization. At present, the clinical practice guideline information platform can directly or indirectly solve the problems related to the lengthy revision cycles, decentralized dissemination and limited application of clinical practice guidelines. Therefore, this paper systematically examines different types of clinical practice guideline information platforms and investigates their corresponding challenges and emerging trends in platform design, data integration, and practical implementation, with the aim of clarifying the current status of this field and providing valuable reference for future research on clinical practice guideline information platforms.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

ObjectiveTo analyze the clinical treatment plan and traditional Chinese medicine （TCM） medication rule of children with primary nephrotic syndrome （PNS） in the First Affiliated Hospital of Henan University of Chinese Medicine. MethodsThe gender and age of children firstly diagnosed with nephrotic syndrome in the pediatric nephrology department of the First Affiliated Hospital of Henan University of Chinese Medicine from November 2019 to December 2022 were collected， and the use of immunosuppressive agents and related frequencies were counted. According to the inclusion and exclusion criteria， an independent TCM prescription database for children with nephrotic syndrome was established. Excel was used to analyze the relevant information of the literature. The frequency counting， association rule analysis， and cluster analysis were carried out on TCM in the prescription， and the high-frequent drugs were analyzed. Results（1） General information： A total of 711 children were included， consisting of 522 males （73.42%） and 189 females （26.58%）. The ratio of male to female was about 2.76∶1. The disease mainly occurred in infants and preschool age， and the average age of onset was （4.74 ± 3.48） years old. （2） Clinical treatment plan and use of immunosuppressive agents： Of the 711 children with PNS， 237 were treated with hormone alone （32.33%）， and 474 （66.67%） received immunosuppressive agents combined with hormones. In the initial treatment， hormone combined with Tacrolimus （TAC） was the preferred treatment （32.91%）. For children with refractory PNS who exhibited poor clinical efficacy， Rituximab （RTX） was mostly used for treatment， with a ratio of up to 23.63%. （3） TCM syndrome and medication rule： In PNS syndrome differentiation， Qi and Yin deficiency was identified as the main syndrome. This involved a total of 477 cases， accounting for 67.09%. Yang deficiency of spleen and kidney was observed in 118 cases， accounting for 16.60%. A total of 711 children were included， of which 706 children were treated with TCM. This involved a total of 706 prescriptions， 226 TCM， and 9 793 frequencies. There were 30 herbs used more than 95 times. The top five TCM were Radix et Rhizoma Glycyrrhizae （81.16%）， Radix Astragali （71.81%）， Poria （68.84%）， Rhizoma Atractylodis Macrocephalae （63.60%）， and Fructus Corni （57.37%）. The drug association rules and network diagram showed that the combination of ''Radix Astragali-Rhizoma Atractylodis Macrocephalae-Poria'' was the closest， and five types of combinations were obtained by cluster analysis. ConclusionIn the diagnosis and treatment of PNS in children， TAC combined with hormones shows good clinical efficacy and high safety. For children with refractory PNS， RTX combined with hormones can be used. TCM medication for PNS should follow the basic principles of strengthening the body and vital Qi and make good use of drugs such as Radix Astragali， Poria， Rhizoma Atractylodis Macrocephalae， and cornus to regulate the Yin and Yang balance and achieve better clinical efficacy.

ObjectiveTo analyze the clinical treatment plan and traditional Chinese medicine （TCM） medication rule of children with primary nephrotic syndrome （PNS） in the First Affiliated Hospital of Henan University of Chinese Medicine. MethodsThe gender and age of children firstly diagnosed with nephrotic syndrome in the pediatric nephrology department of the First Affiliated Hospital of Henan University of Chinese Medicine from November 2019 to December 2022 were collected， and the use of immunosuppressive agents and related frequencies were counted. According to the inclusion and exclusion criteria， an independent TCM prescription database for children with nephrotic syndrome was established. Excel was used to analyze the relevant information of the literature. The frequency counting， association rule analysis， and cluster analysis were carried out on TCM in the prescription， and the high-frequent drugs were analyzed. Results（1） General information： A total of 711 children were included， consisting of 522 males （73.42%） and 189 females （26.58%）. The ratio of male to female was about 2.76∶1. The disease mainly occurred in infants and preschool age， and the average age of onset was （4.74 ± 3.48） years old. （2） Clinical treatment plan and use of immunosuppressive agents： Of the 711 children with PNS， 237 were treated with hormone alone （32.33%）， and 474 （66.67%） received immunosuppressive agents combined with hormones. In the initial treatment， hormone combined with Tacrolimus （TAC） was the preferred treatment （32.91%）. For children with refractory PNS who exhibited poor clinical efficacy， Rituximab （RTX） was mostly used for treatment， with a ratio of up to 23.63%. （3） TCM syndrome and medication rule： In PNS syndrome differentiation， Qi and Yin deficiency was identified as the main syndrome. This involved a total of 477 cases， accounting for 67.09%. Yang deficiency of spleen and kidney was observed in 118 cases， accounting for 16.60%. A total of 711 children were included， of which 706 children were treated with TCM. This involved a total of 706 prescriptions， 226 TCM， and 9 793 frequencies. There were 30 herbs used more than 95 times. The top five TCM were Radix et Rhizoma Glycyrrhizae （81.16%）， Radix Astragali （71.81%）， Poria （68.84%）， Rhizoma Atractylodis Macrocephalae （63.60%）， and Fructus Corni （57.37%）. The drug association rules and network diagram showed that the combination of ''Radix Astragali-Rhizoma Atractylodis Macrocephalae-Poria'' was the closest， and five types of combinations were obtained by cluster analysis. ConclusionIn the diagnosis and treatment of PNS in children， TAC combined with hormones shows good clinical efficacy and high safety. For children with refractory PNS， RTX combined with hormones can be used. TCM medication for PNS should follow the basic principles of strengthening the body and vital Qi and make good use of drugs such as Radix Astragali， Poria， Rhizoma Atractylodis Macrocephalae， and cornus to regulate the Yin and Yang balance and achieve better clinical efficacy.

To introduce and analyze guideline terminology related to clinical question formulation, evidence retrieval and appraisal, and recommendation development.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

As the application of immune checkpoint inhibitors(ICIs)in the perioperative treatment of melanoma is increasingly introduced at earlier stages,it presents a critical opportunity for the development and clinical translation of neoadjuvant therapy.The results of phaseⅠ/Ⅱ clinical trials on neoadjuvant ICI therapy for melanoma demonstrate that neoadjuvant ICIs effectively improve the pathologic re-sponse rate in melanoma patients.Recent studies have shown that combining ICIs with other treatment modalities,including radiotherapy,chemotherapy,and targeted therapies,can enhance antitumor efficacy of neoadjuvant treatment for patients with melanoma.Optimizing treatment regimens,managing adverse events,identifying and addressing pseudoprogression,and handling cases of oligoprogression have become key areas of research in incorporating ICI regimens into neoadjuvant treatment for patients with melanoma.The search for bio-markers to monitor immunotherapy efficacy is expected to become a major focus of future research.This article provides a review of the re-search progress,controversies,and challenges in the application of ICIs in the neoadjuvant treatment of melanoma,and discusses future re-search directions,aiming to offer insights into the clinical application and development of ICIs in melanoma neoadjuvant therapy.