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 develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

The eIF3i gene was amplified from lamb testicular(LT)cells by PCR and cloned into pCold vector to construct the pCold-eIF3i plasmid.Plasmid PCR,double enzyme digestion and se-quencing were used to verify the results.The recombinant eIF3i protein was induced under the op-timized expression conditions.The expression and reactogenicity of the target protein were detected by SDS-PAGE and Western blot.New Zealand white rabbits were immunized with purified recom-binant eIF3i protein combined with Freund's complete and incomplete adjuvants for three times.Se-rum samples were collected after immunization.Indirect ELISA was used to detect antiserum titer,and Western blot was used to analyze antibody specificity.Indirect immunofluorescence assay(IFA)was used to detect the application effect of antibodies.The results showed that the size of LT eIF3i gene was 978 bp.The optimal expression conditions for the eIF3i recombinant protein were as follows:IPTG concentration of 0.2 mmol/L,temperature of 37 ℃,and induction time of 8 h.The recombinant eIF3i protein was expressed as an inclusion body with a size of about 36 kDa.The titer of polyclonal antibody against eIF3i protein was 1∶51 200.Western blot and IFA showed that the prepared polyclonal antibody against eIF3i protein had good reactivity and specificity.In conclusion,we successfully prepared rabbit anti-eif3i polyclonal antibody and confirmed that it could specifically recognize endogenous eIF3i protein,which laid a foundation for further study on the biological function of eIF3i protein.

This study aims to establish a convenient,new and visual detection method for the field diagnosis of Pasteurella multocida(Pm).With reference to the Pm kmt1 gene conserved sequence published in GenBank,PCR amplification primers were designed,the amplified kmt1 gene was cloned into pMD19-T vector,and the recombinant plasmid standard pMD19-T-kmt 1 was estab-lished and identified by PCR and sequencing.Using pMD1 9-T-kmt 1 plasmid as template and kmt1 gene as target gene,basic primers were designed and synthesized.According to the requirements of LFD,a probe(Pm-P)was designed,and the RPA-LFD method for Pm detection was established by optimizing the reaction conditions.Specificity and sensitivity tests were carried out,and 64 clini-cal samples were tested by the method.The results showed that the established Pm RPA-LFD method could be amplified at 37 ℃ for 15 min.Escherichia coli(E.coli),Salmonella enteriditis(SE),Riemerella anatipestifer(RA),Staphylococcus,goose parvovirus(GPV),duck plague virus(DPV),Muscovy duck parvovirus(MDPV)DNA was extracted as the template,and plasmid standard pMD19-T-kmt 1 was used as the positive control.All the positive controls were negative,indicating that the method had good specificity.The plasmid standard pMD1 9-T-kmt 1 was diluted with a 10-fold ratio,and the plasmid standard with a concentration of 107-100 copies/μL was used as the template.The sensitivity was 1.50×101 copies/μ,,which was 100 times higher than that of PCR.A total of 64 clinical samples with suspected RA were subjected to testing using PCR,RPA and LAMP-LFD,with a 100％compliance rate for all three detection tests.The results show that the established RPA-LFD method has the characteristics of strong specificity,high sensitivity,fast speed and visualization,and can be applied to the field detection of Pm.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Aim To explore the mechanism of Cong Rong San on AD model rats based on protein kinase R-like endoplasmic reticulum kinase(PERK)-eukaryotic initiation factor 2α(eIF2α)-nuclear factor kappa B(NF-κB)signaling pathway.Methods Sixty mice were randomly divided into normal group,model group,Cong Rong San groups(4.62,9.24,18.48 g·kg-1)and donepezil group,with 10 mice in each group.All groups of rats received bilateral hippocampal injections of Aβ1-42 to establish the AD model,except the normal group.After the intragastric administration,the Morris water maze behavior test was performed for rats to test-ed the learning and memory abilities.Nissl staining was detected the quantity and Nissl bodies of nerve cells.To detect the nuclear translocation of NF-κB by immu-nofluorescence.To observe the ultrastructure of endo-plasmic reticulum by Transmission electron microsco-py.ELISA for Aβ1-42 and inflammatory cytokines quantification.Western blot was used to detect the ex-pression level of protein in the hippocampus in PERK-eIF2α-NF-κB signaling pathway.Results The morris water maze results showed that Cong Rong San im-proved the escape latency time,increased the number of platform crossings,and prolonged the time spent in the target quadrant in AD rats.(P＜0.05 or P＜0.01).Nissl staining shows the neuronal cells are ar-ranged neatly,nucleus are present and the number of Nissl bodies was numerous and the number of neurons was increased in various doses of Cong Rong San.Im-munofluorescence showed that the expression of NF-κB in the nucleus of rats was decreased(P＜0.05 or P＜0.01).The shape of endoplasmic reticulum was neat,no significantly expanded,and the structure was normal in various doses of Cong Rong San.The levels of Aβ1-42,IL-1,TNF-α and the ratio of p-PERK/PERK,p-eIF2α/eIF2α,p-NF-κB p65/NF-κB p65 in hippo-campus of Cong Rong San group was significantly de-creased in ELISA and Western blot test(P＜0.05 or P＜0.01).Conclusion Cong Rong San can alleviates the immune inflammatory response of neuronal cells in the ERS state for improve the learning and memory a-bility of AD rats,the mechanism of action may through restraint the activation of PERK-eIF2α-NF-κB signa-ling pathway.

Objective To carry out cervical cancer VMAT radiotherapy planning respectively with the single-criteria optimization(SCO)and multi-criteria optimization(MCO)modes of Raystation 4.7 planning system,and to provide references for selecting optimization mode clinically by comparing and analyzing the dosimetric parameters such as target dose distribution,exposure dose to organ at risk(OAR),monitor unit and beam-on time.Methods Ten cervical cancer patients who attended some hospital from February to December 2022 were retrospectively selected,and some VMAT plans were designed for them with the SCO mode of Raystation 4.7 planning system and then enrolled into a SCO group;other VMAT plans were redesigned with the MCO mode under the premise the setup conditions such as machine model and shot field angle were unchanged,and divided into a MCO group.The two groups were compared in terms of maximum dose(D2%),minimum dose(D98%),homogeneity index(HI)and conformity index(CI)of the planning target volume(PTV),beam-on time,monitor unit and exposure doses to OARs including bladder V50 Gy,V40 Gy and V30 Gy,rectum V50 Gy,V40 Gy and V30 Gy,small intestine V50 Gy,V40 Gy and V30 Gy and left and right femur V50 Gy,V40 Gy and V30 Gy.SPSS 22.0 software was used for statistical analysis.Results Both the two groups met clinical requirements.There were no significant differences between the two groups in D98%,CI,small intestine V50 Gy and left and right femur V40 Gy(P>0.05).The MCO group had the values of HI,D2%,V50 Gy,V40 Gy and V30 Gy,rectum V50 Gy,V40 Gy and V30 Gy,small intestine V40 Gy and V30 Gy and left and right femur V30 Gy lower than those of the SCO group,with the differences being statistically significant(P<0.05).The SCO group had less monitor units and shorter beam-on time when compared with the MCO group,with the differences being statistically siginificant(P<0.05).Conclusion When compared with the SCO mode-based VMAT plans,the MCO mode-based VMAT plans significantly decrease the exposure doses and volume to rectum,bladder and small intestine and raise the PTV homogeneity,while lower the treatment efficiency to some extent by increased monitor units and prolonged beam-on time.[Chinese Medical Equipment Journal,2025,46(9):39-44]

Objective To carry out cervical cancer VMAT radiotherapy planning respectively with the single-criteria optimization(SCO)and multi-criteria optimization(MCO)modes of Raystation 4.7 planning system,and to provide references for selecting optimization mode clinically by comparing and analyzing the dosimetric parameters such as target dose distribution,exposure dose to organ at risk(OAR),monitor unit and beam-on time.Methods Ten cervical cancer patients who attended some hospital from February to December 2022 were retrospectively selected,and some VMAT plans were designed for them with the SCO mode of Raystation 4.7 planning system and then enrolled into a SCO group;other VMAT plans were redesigned with the MCO mode under the premise the setup conditions such as machine model and shot field angle were unchanged,and divided into a MCO group.The two groups were compared in terms of maximum dose(D2%),minimum dose(D98%),homogeneity index(HI)and conformity index(CI)of the planning target volume(PTV),beam-on time,monitor unit and exposure doses to OARs including bladder V50 Gy,V40 Gy and V30 Gy,rectum V50 Gy,V40 Gy and V30 Gy,small intestine V50 Gy,V40 Gy and V30 Gy and left and right femur V50 Gy,V40 Gy and V30 Gy.SPSS 22.0 software was used for statistical analysis.Results Both the two groups met clinical requirements.There were no significant differences between the two groups in D98%,CI,small intestine V50 Gy and left and right femur V40 Gy(P>0.05).The MCO group had the values of HI,D2%,V50 Gy,V40 Gy and V30 Gy,rectum V50 Gy,V40 Gy and V30 Gy,small intestine V40 Gy and V30 Gy and left and right femur V30 Gy lower than those of the SCO group,with the differences being statistically significant(P<0.05).The SCO group had less monitor units and shorter beam-on time when compared with the MCO group,with the differences being statistically siginificant(P<0.05).Conclusion When compared with the SCO mode-based VMAT plans,the MCO mode-based VMAT plans significantly decrease the exposure doses and volume to rectum,bladder and small intestine and raise the PTV homogeneity,while lower the treatment efficiency to some extent by increased monitor units and prolonged beam-on time.[Chinese Medical Equipment Journal,2025,46(9):39-44]

Objective To explore the application value of MRI diaphragmatic navigation technology combined with three dimensional liver acquisition with volume acceleration-flexible(3D LAVA-FLEX)sequence in abdominal enhanced imaging of infants and young children.Methods A retrospective analysis was conducted on imaging data of 84 infants and young children who underwent abdominal enhanced MRI examination in our hospital between January 2021 and December 2023.All 84 infants and young children initially underwent conventional dynamic contrast-enhanced 3D LAVA-FLEX sequence scanning;the delayed phase images obtained were included in the dynamic enhancement group.Subsequently,diaphragmatic navigation combined with 3D LAVA-FLEX sequence examination was implemented,and the obtained images were included in the diaphragm navigation group.Subjective scoring was performed for images in both groups,while the signal to noise ratio(SNR),contrast to noise ratio(CNR),and artifact quantification(AQ)were measured and compared between the two groups.Results The respiratory motion artifacts,the clarity of liver parenchyma enhancement,the clarity of liver vascular enhancement,the clarity of spleen parenchyma enhancement and the overall image quality score in the diaphragm navigation group were higher than those in the dynamic enhancement group,and the differences were statistically significant(P<0.05).There were statistically significant differences in SNR and AQ between the two groups of images(P<0.000 1),while there was no statistically significant difference in CNR between the two groups of images(P>0.05).Conclusion Diaphragmatic navigation technology combined with 3D LAVA-FLEX sequence imaging can improve the image quality of abdominal MRI enhanced imaging in infants and young children,and provide a reference for clinical diagnosis and treatment.

The eIF3i gene was amplified from lamb testicular(LT)cells by PCR and cloned into pCold vector to construct the pCold-eIF3i plasmid.Plasmid PCR,double enzyme digestion and se-quencing were used to verify the results.The recombinant eIF3i protein was induced under the op-timized expression conditions.The expression and reactogenicity of the target protein were detected by SDS-PAGE and Western blot.New Zealand white rabbits were immunized with purified recom-binant eIF3i protein combined with Freund's complete and incomplete adjuvants for three times.Se-rum samples were collected after immunization.Indirect ELISA was used to detect antiserum titer,and Western blot was used to analyze antibody specificity.Indirect immunofluorescence assay(IFA)was used to detect the application effect of antibodies.The results showed that the size of LT eIF3i gene was 978 bp.The optimal expression conditions for the eIF3i recombinant protein were as follows:IPTG concentration of 0.2 mmol/L,temperature of 37 ℃,and induction time of 8 h.The recombinant eIF3i protein was expressed as an inclusion body with a size of about 36 kDa.The titer of polyclonal antibody against eIF3i protein was 1∶51 200.Western blot and IFA showed that the prepared polyclonal antibody against eIF3i protein had good reactivity and specificity.In conclusion,we successfully prepared rabbit anti-eif3i polyclonal antibody and confirmed that it could specifically recognize endogenous eIF3i protein,which laid a foundation for further study on the biological function of eIF3i protein.