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.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Consciousness, which underpins human perception, cognition, and behavioural responses, has long been a major focus of neuroscience and medicine. Alterations in consciousness occur not only during reversible processes such as sleep and anaesthesia, but also in pathological conditions, including disorders of consciousness (DOC) and brain injury. With recent advances in the synchronized acquisition and analysis of multimodal physiological signals, such as electroencephalography (EEG) and electrocardiography (ECG), the dynamic interactions between the heart and brain have become increasingly apparent. A growing body of evidence indicates that heart-brain interactions are closely associated with the maintenance and transition of consciousness states, offering a novel perspective for elucidating their underlying mechanisms and supporting relevant clinical assessments. Current approaches to heart-brain interaction analysis for consciousness-state assessment can be broadly grouped into three categories: ECG-based methods, EEG-based methods, and heart-brain coupling analysis. These approaches characterize heart-brain interactions during changes in consciousness from the complementary perspectives of autonomic regulation, cortical interoceptive processing, and cross-system information transfer, respectively. Nevertheless, this field remains at an early stage. Most studies have focused on a single experimental context or a fixed analytical method, with insufficient systematic consideration of the applicability and limitations of different approaches. Moreover, the mechanisms underlying the emergence and alteration of consciousness remain incompletely understood. Accordingly, this review examines heart-brain interaction methods employed in recent consciousness-state assessment studies in accordance with progressively deeper analytical perspectives, and systematically evaluates the advantages and limitations of these methods across distinct consciousness-related conditions. Heart rate variability (HRV) is one of the classical ECG-based indices used to investigate heart-brain interactions. It refers to fluctuations in consecutive interbeat intervals over a specified time period and is regarded as a physiological measure of autonomic nervous system regulation that may indirectly reflect the strength of heart-brain interactions. Depending on the analytical dimension, HRV analysis is commonly divided into time-domain, frequency-domain, and complexity-index analyses. These approaches characterize autonomic activity in terms of temporal fluctuations, the distribution of frequency components, and nonlinear dynamical properties, respectively. Although extensive research supports their physiological interpretation, they cannot characterize directional interactions from the heart to the brain. Among EEG-based methods for heart-brain interaction analysis, EEG activity directly related to cardiac fluctuations has received particular attention. One of the most widely used measures is the heartbeat-evoked potential (HEP). HEP-based approaches are considered to directly reflect the capacity of the cerebral cortex to process cardiac afferent signals and may be especially advantageous for characterizing rapidly changing consciousness levels, such as those observed during sleep. However, they do not comprehensively capture dynamic bidirectional information exchange between the brain and heart and are susceptible to cardiac artefacts, respiration, and variation in preprocessing procedures. Heart-brain coupling analysis quantifies functional associations between ECG and EEG through cross-system interaction measures, thereby assessing dynamic interactions between the heart and brain. It emphasizes information transfer, feedback regulation, and cross-timescale coordination between the two systems, offering a more comprehensive perspective for investigating changes in consciousness during sleep, anaesthesia, and DOC. However, the neurophysiological mechanisms and physiological interpretation of individual computational indices have not yet been fully established, and the robustness of their results and their potential for clinical generalization require further validation. Finally, this review summarizes the commonalities and differences among the three classes of methods across research domains involving different consciousness states. It further argues that heart-brain interaction research requires deeper investigation at both the mechanistic level of consciousness and the level of clinical application. Current challenges and key issues are identified, and future directions and feasible pathways for heart-brain interaction research are discussed, with the aim of providing new perspectives for researchers in related fields.

Objective: This study aims to evaluate the clinical benefits of the integrated optical and magnetic surgical navigation system in assisting transforaminal endoscopic lumbar discectomy (TELD) for the treatment of lumbar disc herniation (LDH). Methods: A retrospective analysis was conducted on patients who underwent TELD for LDH at Beijing Chaoyang Hospital, Capital Medical University from November 2022 to December 2023. Patients treated with the integrated optical and magnetic surgical navigation system were defined as the navigation-guided TELD (Ng-TELD) group (30 cases), while those treated with the conventional x-ray fluoroscopy method were defined as the control group (31 cases). Record and compare baseline characteristics, surgical parameters, efficacy indicators, and adverse events between the 2 patient groups. Results: The average follow-up duration for the 61 patients was 11.8 months. Postoperatively, both groups exhibited significant relief from back and leg pain, which continued to improve over time. At the final follow-up, patients’ lumbar function and quality of life had significantly improved compared to preoperative levels (p < 0.05). The Ng-TELD group had significantly shorter total operation time (58.43 ± 12.37 minutes vs. 83.23 ± 25.90 minutes), catheter placement time (5.83 ± 1.09 minutes vs. 15.94 ± 3.00 minutes), decompression time (47.17 ± 11.98 minutes vs. 67.29 ± 24.23 minutes), and fewer intraoperative fluoroscopies (3.20 ± 1.45 vs. 16.58 ± 4.25) compared to the control group (p < 0.05). There were no significant differences between the groups in terms of efficacy evaluation indicators and hospital stay. At the final follow-up, the excellent and good rate of surgical outcomes assessed by the MacNab criteria was 98.4%, and the overall adverse event rate was 8.2%, with no statistically significant differences between the groups (p > 0.05). Conclusion This study demonstrates that the integrated optical and magnetic surgical navigation system can reduce the complexity of TELD, shorten operation time, and minimize radiation exposure for the surgeon, highlighting its promising clinical potential.

Objective: This study aims to evaluate the clinical benefits of the integrated optical and magnetic surgical navigation system in assisting transforaminal endoscopic lumbar discectomy (TELD) for the treatment of lumbar disc herniation (LDH). Methods: A retrospective analysis was conducted on patients who underwent TELD for LDH at Beijing Chaoyang Hospital, Capital Medical University from November 2022 to December 2023. Patients treated with the integrated optical and magnetic surgical navigation system were defined as the navigation-guided TELD (Ng-TELD) group (30 cases), while those treated with the conventional x-ray fluoroscopy method were defined as the control group (31 cases). Record and compare baseline characteristics, surgical parameters, efficacy indicators, and adverse events between the 2 patient groups. Results: The average follow-up duration for the 61 patients was 11.8 months. Postoperatively, both groups exhibited significant relief from back and leg pain, which continued to improve over time. At the final follow-up, patients’ lumbar function and quality of life had significantly improved compared to preoperative levels (p < 0.05). The Ng-TELD group had significantly shorter total operation time (58.43 ± 12.37 minutes vs. 83.23 ± 25.90 minutes), catheter placement time (5.83 ± 1.09 minutes vs. 15.94 ± 3.00 minutes), decompression time (47.17 ± 11.98 minutes vs. 67.29 ± 24.23 minutes), and fewer intraoperative fluoroscopies (3.20 ± 1.45 vs. 16.58 ± 4.25) compared to the control group (p < 0.05). There were no significant differences between the groups in terms of efficacy evaluation indicators and hospital stay. At the final follow-up, the excellent and good rate of surgical outcomes assessed by the MacNab criteria was 98.4%, and the overall adverse event rate was 8.2%, with no statistically significant differences between the groups (p > 0.05). Conclusion This study demonstrates that the integrated optical and magnetic surgical navigation system can reduce the complexity of TELD, shorten operation time, and minimize radiation exposure for the surgeon, highlighting its promising clinical potential.

Objective: This study aims to evaluate the clinical benefits of the integrated optical and magnetic surgical navigation system in assisting transforaminal endoscopic lumbar discectomy (TELD) for the treatment of lumbar disc herniation (LDH). Methods: A retrospective analysis was conducted on patients who underwent TELD for LDH at Beijing Chaoyang Hospital, Capital Medical University from November 2022 to December 2023. Patients treated with the integrated optical and magnetic surgical navigation system were defined as the navigation-guided TELD (Ng-TELD) group (30 cases), while those treated with the conventional x-ray fluoroscopy method were defined as the control group (31 cases). Record and compare baseline characteristics, surgical parameters, efficacy indicators, and adverse events between the 2 patient groups. Results: The average follow-up duration for the 61 patients was 11.8 months. Postoperatively, both groups exhibited significant relief from back and leg pain, which continued to improve over time. At the final follow-up, patients’ lumbar function and quality of life had significantly improved compared to preoperative levels (p < 0.05). The Ng-TELD group had significantly shorter total operation time (58.43 ± 12.37 minutes vs. 83.23 ± 25.90 minutes), catheter placement time (5.83 ± 1.09 minutes vs. 15.94 ± 3.00 minutes), decompression time (47.17 ± 11.98 minutes vs. 67.29 ± 24.23 minutes), and fewer intraoperative fluoroscopies (3.20 ± 1.45 vs. 16.58 ± 4.25) compared to the control group (p < 0.05). There were no significant differences between the groups in terms of efficacy evaluation indicators and hospital stay. At the final follow-up, the excellent and good rate of surgical outcomes assessed by the MacNab criteria was 98.4%, and the overall adverse event rate was 8.2%, with no statistically significant differences between the groups (p > 0.05). Conclusion This study demonstrates that the integrated optical and magnetic surgical navigation system can reduce the complexity of TELD, shorten operation time, and minimize radiation exposure for the surgeon, highlighting its promising clinical potential.

The cribriform fascia is formed by the fusion of deep and superficial fascias in the axilla,and it forms the base of the axilla together with the subcutaneous tissue and skin,which is a key part of the system of the membranous anatomy of breast surgery.The loose anatomical structure of the cribriform fascia makes it an important anatomical landmark in the axillary region,which plays an important role in accurate localization of lymph nodes.The sentinel lymph node biopsy through cribriform fascia has less intraoperative blood loss and shorter catheterization time compared with the traditional axillary fold approach.However,the anatomical structure of the cribriform fascia and its significance in the membranous anatomy of breast surgery have not been fully elucidated.This article analyzes the anatomical structure of the cribriform fascia and its adjacent tissues,and emphasizes the anatomical structure that needs to be paid attention to in sentinel lymph node biopsy through the axillary cribriform fascia,so as to provide a reference for clinicians to fully understand the cribriform fascia structure.

OBJECTIVE T o explore the effectiveness and safety of azvudine in prevention of SARS-CoV-2 infection among the population of close contact with families with symptomatic SARS-CoV-2 infection.METHODS A total of 64 people who were closely contact with the families with symptomatic SARS-CoV-2 infection and were trea-ted in Ma'anshan People's Hospital from Jan.2023 to Oct.2023 were enrolled in the study and randomly divid-ed into the group A with 22 cases,the group B with 17 cases and the group C with 25 cases.The group A was giv-en 5mg of azvudine,the group B was given 3mg of azvudine plus 2mg of placebo,and the group C was given 5 mg of azvudine;all the three groups were treated for 7 days in total.The incidence of SARS-CoV-2 infection and the levels of interleukin-6(IL-6),C-reactive protein(CRP),immunoglobulin G(IgG)and immunoglobulin M(IgM)were observed among the three groups of research subjects after the drug therapies;the adverse reactions were re-corded during the research period.RESULTS The incidence rate of SARS-CoV-2 infection was 4.55％in the group A,11.76％in the group B,12.00％in the group C,and there was no significant difference(x2=0.921,P=0.631).There were no significant differences in the levels of IL-6,CRP,IgG and IgM among the three groups be-fore and after the drug therapies.The incidence of adverse reactions such as fatigue,cough,hypertriglyceridemia and gastrointestinal tract reactions was 9.09％in the group A after the drug therapies,11.76％in the group B,12.00％in the group C,and there was no significant difference.CONCLUSION There is no significant difference in the effectiveness of azvudine in prevention of SARS-CoV-2 infection among the population of close contact with families with symptomatic SARS-CoV-2 infection,with the safety and tolerance favorable.

Objective To analyze the trends in incidence and mortality of breast cancer among Chinese women from 1992 to 2021,assess the impact of age,period,and cohort on its incidence and mortality rates,and predict future trends to provide a basis for developing effective intervention strategies.Methods Utilizing the 2021 Global Burden of Disease(GBD2021)database,the Joinpoint regression model was employed to analyze the trends in age-standardized incidence and mortality rates of breast cancer among Chinese women from 1992 to 2021.The age-period-cohort model was applied to estimate the age,period,and cohort effects on the incidence and mortality of breast cancer among Chinese women during the same period.The autoregressive integrated moving average(ARIMA)model was used to predict the age-standardized incidence and mortality rates of breast cancer among Chinese women from 2022 to 2026.A stratified analysis was conducted to explore the impact of different risk factors[including smoking,alcohol consumption,high body mass index(BMI),hyperglycemia,physical inactivity,and diet]on breast cancer mortality.Results From 1992 to 2021,the incidence and mortality rates of breast cancer among Chinese women showed an overall upward trend,with incidence rates rising from 15.95/100,000 in 1992 to 55.54/100,000 in 2021,and mortality rates increasing from 7.35/100,000 to 12.41/100,000.The age-standardized incidence rate also exhibited an upward trend,rising from 18.51/100,000 to 37.00/100,000,with an average annual percentage change(AAPC)of 2.43%.However,the age-standardized mortality rate showed an overall downward trend,decreasing from 9.05/100,000 to 8.24/100,000,with an AAPC of-0.35%.The APC model analysis revealed that the age,period,and cohort effects on incidence and mortality were statistically significant(P<0.001).Within the same birth cohort,breast cancer incidence increased in women aged 15-89 years but decreased in those≥90 years.Breast cancer mortality showed a steady increase with age.With the increase in years,the risk of breast cancer incidence gradually increased,reaching the highest between 2017 and 2021,with a relative risk(RR)value of 1.37.Conversely,the risk of breast cancer mortality decreased with the increase in years,with the lowest mortality between 2012 and 2016,and an RR value of 0.86.With the increase in the birth cohort year,the risk of breast cancer incidence gradually increased,while the risk of mortality gradually decreased.The ARIMA model prediction results showed that the age-standardized incidence rate of breast cancer among women would continue to rise from 2022 to 2026,reaching 40.25/100,000 by 2026,while the age-standardized mortality rate would tend to stabilize at 8.28/100,000 by 2026.Among the risk factors for breast cancer,diet was found to have the highest impact on breast cancer mortality.Conclusions The incidence rate of breast cancer among Chinese women continues to rise,indicating that the prevention and control situation remains severe.Future efforts should focus on developing precise screening programs for high-risk populations and optimizing early screening strategies and treatment resource allocation based on predicted trend.

Objective To analyze the effect of Hsa-circ-0101216 on gemcitabine(GEM)chemotherapy resistance in pancreatic cancer and its mechanism.Methods Differentially expressed circRNAs between GEM-resistant pancreatic cancer cells and parent cells were screened using the GEO database.Pancreatic cancer GEM resistant cell lines(BxPC-3-GEM and Capan-1-GEM)were constructed by intermittent concentration gradient method.qRT-PCR was used to detect the expression of Hsa-circ-0101216 in cells.GEM resistant pancreatic cancer cell lines were taken and divided into sh-circ-0101216 group(knockdown of circ-0101216),sh-NC group(transfected with sh-NC),and blank control group(untreated).CCK-8 assay and EdU proliferation assay were used to detect the half inhibitory concentration(IC50)of GEM and proliferation ability of cells in each group.Western blotting was performed to detect the expression of multidrug resistance-related protein 1(MRP1),breast cancer resistance protein(BCRP),and human equilibrative nucleoside transporter-1(hENT-1).A subcutaneous xenograft tumor model of human pancreatic cancer in nude mice was constructed,and sh-NC+GEM group and sh-circ-0101216+GEM group(n=6)were set up.The volume and weight of xenograft tumor in nude mice were compared between the two groups.Western blotting and immunohistochemistry were used to detect the expression of MRP1,BCRP,and hENT-1 proteins in xenograft tumor tissues,and EDU proliferation assay was used to detect the proliferation ability of tumor cells.Results The GEO database screening showed that Hsa-circ-0101216 was up-regulated in GEM-resistant pancreatic cancer cell lines.Pancreatic cancer GEM-resistant cell lines were successfully constructed,and the expression levels of Hsa-circ-0101216 and the IC50 value in GEM-resistant pancreatic cancer cells BxPC-3-GEM and Capan-1-GEM were significantly higher than those in parental cells(P<0.05).In sh-circ-0101216 group,the IC50 values of GEM,cell viability,EdU positivity rate,and the expression levels of MRP1 and BCRP proteins in GEM-resistant pancreatic cancer cells BxPC-3-GEM and Capan-1-GEM were significantly lower than those in blank control group and sh-NC group,while the expression level of hENT-1 protein was significantly higher(P<0.05 or P<0.001).In sh-circ-0101216+GEM group,the weight and volume of subcutaneous xenograft tumors in nude mice,the expression levels and positive expression rates of MRP1 and BCRP proteins in tumor tissues,and the EdU positive rate were significantly lower than those in sh-NC+GEM group,while the expression level and positive expression rate of hENT-1 protein were significantly higher(P<0.05).Conclusions Hsa-circ-0101216 is highly expressed in GEM-resistant pancreatic cancer cell lines.Its knockdown can inhibit the proliferation of pancreatic cancer cells and enhance the chemosensitivity of pancreatic cancer cells to GEM.The mechanism may be related to the regulation of transmembrane transporter protein expression.