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.

Objective: To investigate the impact of RhE antigen-matched transfusion during liver transplantation on early postoperative recovery and complications. Methods: In this retrospective cohort study, ninety-five patients undergoing liver transplantation at Kunming First People's Hospital between January 2022 and July 2025 were enrolled. Patients were divided into two groups: Group 1 (RhE-mismatched transfusion, n=57) and Group 2 (RhE-matched transfusion, n=38). The baseline data, complete blood counts, hepatic and renal function, coagulation parameters, and complication rates between the two groups were compared at postoperative days 1, 3, 5, 7, and 10. Survival analysis was performed using the Kaplan-Meier method. Results: The baseline characteristics were well-balanced and comparable between the two groups (all P>0.05). The early postoperative mortality rate in the mismatched group (31.58%, 18/57) was significantly higher than that in the matched group (10.53%, 4/38) (P=0.017). The incidence of postoperative hepatic encephalopathy was significantly higher in the mismatched group (50.88%, 29/57) than in the matched group (10.53%, 4/38) (P<0.001). The incidence of postoperative haemorrhage in the mismatched group (24.56%, 14/57) was higher than that in the matched group (5.26%, 2/38), with a statistically significant difference (P=0.014). The incidence of perioperative infection in the mismatched group (28.07%, 16/57) was higher than that in the matched group (10.53%, 4/38), with a statistically significant difference (P=0.04). Corresponding odds ratios (OR) and 95% confidence intervals indicated a lower risk of these adverse events in the matched group. On postoperative day 1, the change in activated partial thromboplastin time (-1.6, 20.5) in the mismatched group was greater than in the matched group (-0.2, 5.5). The change in international normalised ratio (-0.56, 1.22) in the mismatched group was greater than in the matched group (-0.18, 0.32), while the change in albumin (-4.0, 4.8) was smaller in the mismatched group than in the matched group (-2.5, 8.8). On postoperative day 5, the change in albumin (-0.41±7.83) in the mismatched group was smaller than in the matched group (2.68±4.53). At postoperative day 7, the change in albumin in the mismatched group (-0.61±7.38) was smaller than that in the matched group (2.51±5.85), while the change in D-dimer in the mismatched group (0.73, 7.4) was greater than that in the matched group (-1.6, 4.3). On postoperative day 10, the mismatched group exhibited significantly higher fibrinogen levels (-1.21, 1.78) than the matched group (-0.49, 0.97), and significantly longer prothrombin times (-11.3, -2.7) than the matched group (-6.2, -0.8) (all P<0.05). The matched group exhibited a mean overall survival (OS) of 32.803 months (95% CI:29.171-36.436 months), significantly exceeding the mismatched group's 28.996 months (95% CI:24.202-33.790 months). The log-rank test yielded statistically significant results (χ =4.307, P=0.038). Conclusion: Implementing RhE blood group-matched transfusion during liver transplantation may help reduce early postoperative mortality and the incidence of major complication rates, promote faster recovery of coagulation and liver function, and thereby improve short-term patient outcomes.

Abstract To investigate the intervention effects of digital exercise on insufficient physical activity (PA) and sedentary behavior among college students, aiming to enhance PA levels and reduce sedentary behavior. The study systematically reviews the application characteristics of digital exercise intervention, the intervention effects of digital exercise intervention (training websites and applications, social media, wearable devices, emerging interactive technologies) on PA levels and sedentary behavior among college students.Through behavioral modification elements such as goal setting, self monitoring, and feedback support, the approach significantly enhances college students PA levels and improves sedentary behaviors, so as to provide evidence for the design and optimization of future intervention programs.

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.

A 71-year-old male presented with esophageal cancer and severe aortic valve regurgitation. Treatment strategies for such patients are controversial. Considering the risks of cardiopulmonary bypass and potential esophageal cancer metastasis, we successfully performed transcatheter aortic valve implantation and minimally invasive three-incision thoracolaparoscopy combined with radical resection of esophageal cancer (McKeown) simultaneously in the elderly patient who did not require neoadjuvant treatment. This dual minimally invasive procedure took 6 hours and the patient recovered smoothly without any surgical complications.

Objective To explore the effect of curcumin(CUR)on oxidative damage of keratinocytes induced by ultraviolet B(UVB)irradiation through Toll-like receptor 4(TLR4)/nuclear factor-kappa B(NF-κB)/nucleotide-binding oligomerization domain-containing protein 3(NLRP3)signaling pathway.Methods Human keratinocytes of HaCaT were cultured normally in vitro,and the keratinocyte oxidative damage model was established by the irradiation of 57 mJ/cm2 UVB.The cells with normal culture were as the control group,the cells treated after modeling were as the UVB group,the cells treated with 5 μmol/L CUR after modeling were as the CUR group,the cells treated with 100 μg/L TLR4 inhibitor of TAK-242 after modeling were as the TAK-242 group,and the cells treated with 5 μmol/L CUR and 100 nmol/L TLR4 activator of lipopolysaccharide(LPS)were as the CUR+LPS group.qRT-PCR was applied to detect the relative expression levels of TLR4,NF-κB,and NLRP3 mRNAs of cells in each group.CCK-8 was applied to detect the cell proliferation in each group.The relative content of reactive oxygen species(ROS),the viabilities of superoxide dismutase(SOD)and catalase(CAT),and the concentrations of glutathione(MDA)and glutathione(GSH)of cells in each group were detected by fluorescence assay according to the kit instruction.ELISA kit was used to detect the expression of inflammatory factors of tumor necrosis factor-α(TNF-α)and interleukin-1β(IL-1β)of cells in each group.Flow cytometry was applied to detect the cell apoptosis in each group.Western blot was applied to detect the expression of proliferation related protein of proliferating cell nuclear antigen(PCNA),apoptosis related proteins[B-cell lymphoma-2(Bcl-2)and Bcl-2-associated X protein(Bax)],and TLR4/NF-κB/NLRP3 signaling pathway related proteins(TLR4,NF-κB and NLRP3)of cells in each group.Results Compared with the Control group,the cell survival rate,the expression levels of PCNA and Bcl-2 proteins,the viabilities of SOD and CAT,and the GSH concentration in the UVB group decreased,while the apoptosis rate,the level of Bax protein,the relative content of ROS,the concentration of MDA,the levels of TNF-α and IL-1β,and the mRNA and protein levels of TLR4,NF-κB and NLRP3 increased(P<0.05).Compared with the UVB group,the cell survival rate,the expression levels of PCNA and Bcl-2 proteins,the viabilities of SOD and CAT,and the GSH concentration in the TAK-242 group and CUR group increased,while the apoptosis rate,the level of Bax protein,the relative content of ROS,the concentration of MDA,the levels of TNF-α and IL-1β,and the mRNA and protein levels of TLR4,NF-κB and NLRP3 decreased(P<0.05).Compared with the CUR group,the cell survival rate,the expression levels of PCNA and Bcl-2 proteins,the viabilities of SOD and CAT,and the GSH concentration in the CUR+LPS group decreased,while the apoptosis rate,the level of Bax protein,the relative content of ROS,the concentration of MDA,the levels of TNF-α and IL-1β,and the mRNA and protein levels of TLR4,NF-κB and NLRP3 increased(P<0.05).Conclusion CUR can increase the antioxidant stress level of keratinocytes,alleviate inflammatory response,promote cell proliferation,and improve cell damage caused by UVB irradiation,which may be related to the inhibition of TLR4/NF-κB/NLRP3 signaling pathway.

Objective:To evaluate the effect of acupuncture on postoperative delirium (POD) in diabetic patients undergoing surgery under general anesthesia.Methods:In this randomized controlled trial, 92 diabetic patients of either sex, aged 30-80 yr, with a body mass index of 18-28 kg/m 2, of American Society of Anesthesiologists Physical Status classification Ⅱ or Ⅲ, scheduled for elective surgery under general anesthesia, were divided into 2 groups ( n=46 each) using a table of random numbers: control group (group C) and acupuncture group (group A). Group A received acupuncture at the Baihui (GV20), Shenting (GV24) and Sishencong (EX-HN1) acupoints before anesthesia. The needles were retained for 30 min, with manual stimulation applied every 10 min for 10 s each time. After 4 stimulations, routine anesthesia was carried out. Group C received routine anesthesia only. Regional cerebral oxygen saturation was recorded on admission to the operating room (T 0), after anesthesia induction (T 1), at the start of surgery (T 2), at the end of surgery (T 3), and immediately after tracheal extubation (T 4). The POD developed within 3 days after surgery was assessed. The occurrence of needle-related adverse effects such as fainting, subcutaneous bleeding, and local paresthesia was recorded. Results:Compared with group C, the incidence of POD was significantly reduced, and the regional cerebral oxygen saturation was increased at T 1, 4 in group A ( P<0.05). Conclusions:Acupuncture can decrease the development of POD in diabetic patients undergoing surgery under general anesthesia, which is related to an increase in regional cerebral oxygen saturation.

Objective:This study aimed to examine the association between malnutrition diagnosed by the Global Leadership Initiative on Malnutrition(GLIM)criteria and clinical outcomes in surgical patients,as well as to assess its prognostic impact on postoperative adverse clinical outcomes.Methods:Electronic databases,including PubMed,Embase,Web of Science,CINAHL,Scopus,The Cochrane Library,Clinical Trials,CNKI,Wanfang Data Knowledge Service Platform,and the Chinese Biomedical Literature Database,were systematically searched.Relevant cohort studies utilizing GLIM criteria to preoperatively diagnose malnutrition in surgical inpatients were included.The exposed group comprised surgical patients diagnosed with preoperative malnutrition using GLIM criteria,while the control group consisted of surgically treated patients without malnutrition as per GLIM criteria.Literature quality was evaluated using the Newcastle-Ottawa Scale(NOS),and meta-analysis was performed using Review Manager 5.4 software.Results:Fourteen literatures were included,with a total sample size of 10,045 patients.Meta-analysis revealed that the malnourished group had a higher incidence of postoperative complications compared to the non-malnourished group[risk ratio(RR)=1.81,95%CI:1.66～1.98),P<0.00001].Additionally,the incidence of severe complications was significantly higher in GLIM-diagnosed malnourished patients.The malnourished group exhibited poorer overall survival[hazard ratio(HR)=1.90,95%CI:1.55～2.34,P<0.00001]and disease-free survival[HR=2.25,95%CI:1.02～4.93,P=0.04]compared to the non-malnourished group.Conclusion:GLIM-diagnosed malnutrition is significantly associated with adverse clinical outcomes in surgical patients,increasing postoperative complication rates and reducing overall and disease-free survival.The GLIM criteria demonstrate value in predicting adverse clinical outcomes in this population.Further high-quality studies are warranted to validate these findings.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective To analyze the imaging features of cerebral small vessel disease in patients with type 2 diabetes mellitus by multimodal MRI.Methods The clinical data of 160 patients with cerebral small vessel disease admitted to our hospital from January to December 2020 were retrospectively analyzed.According to whether they were complicated with type 2 diabetes mellitus,they were divided into the diabetic group and the non-diabetic group,with 80 cases in each group.Both groups underwent multimodal MRI scans.And the severity of lacunar infarction,the severity of subcortical and periventricular white matter lesions,white matter integral and cerebral microbleeds of patients in the two groups were compared.Results The severity of lacunar infarction(χ2=34.076,P=0.001),subcortical white matter lesions(χ2=25.000,P=0.001),periventricular white matter lesions(χ2=22.895,P=0.001)and white matter integral(t=12.370,P=0.001)of patients in the diabetic group were significantly higher than those in the non-diabetic group.No cerebral microbleeds were detected in either group of patients.Conclusion Patients with cerebral small vessel disease and type 2 diabetes mellitus show characteristic multimodal MRI changes.The increase in the number of lacunar infarction lesions and the aggravation of white matter lesions can be used as the characteristic imaging basis for the diagnosis of type 2 diabetes mellitus related cerebral small vessel disease.