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

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.

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.

Objective:This study aims to explore the application effect of anticipatory risk intervention combined with health education under LEARNS mode in elderly patients with chronic heart failure(CHF).Methods:This randomized controlled enrolled 126 elderly CHF patients admitted in Panzhihua Central Hospital between January 2022 and February 2023.They were divided into control group(n=63)and intervention group(n=63).All patients received routine nursing care,patients in control group received additional health education under LEARNS mode,compared to those in intervention group receiving additional anticipatory risk intervention based on control group,both groups were intervened for 2 months.Physiological indexes,negative emotion,quality of life,sleep quality,disease perception&control and self-efficacy,as well as incidence of adverse events,were compared between two groups.Results:Compared with patients in control group after intervention,those in intervention group had signif-icant lower systolic blood pressure(SBP)[(119.84±8.60)mmHg vs.(129.49±9.24)mmHg],diastolic blood pressure(DBP)[(81.59±5.08)mmHg vs.(88.51±6.36)mmHg],N terminal pro brain natriuretic peptide(NT-proBNP)[(2604.46±204.80)ng/L vs.(3024.87±212.27)ng/L],scores of Self-Rating Anxiety Scale(SAS)[(55.90±2.43)points vs.(60.11±2.62)points],Self-Rating Depression Scale(SDS)[(57.03±2.56)points vs.(61.54±2.66)points],Minnesota Living with Heart Failure Questionnaire(MLHFQ)[(52.05±6.32)points vs.(60.46±7.10)points]and Pittsburgh Sleep Quality Index(PSQI)[(9.00±1.65)points vs.(11.05±1.96)points],and significant higher left ventricular ejection fraction(LVEF)[(51.83±7.28)％vs.(47.57±5.98)％],scores of Control Attitude Scale-Revised(CAS-R)[(35.59±2.97)points vs.(30.60±3.52)points]and Gen-eral Self-Efficacy Scale(GSES)[(36.98±4.21)points vs.(30.52±4.14)points](P＜0.001 all).Incidence of adverse events in intervention group was significantly lower than that of control group(3.17％vs.12.70％,P=0.048).Conclusion:Anticipatory risk intervention combined with health education under the LEARNS model could effectively improve the physiological indexes,quality of life and sleep,enhance disease perception and self-effica-cy,and reduce negative emotion and incidence of adverse events in elderly CHF patients.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

A method for determination and targeted screening of diflorasone components in health products using ultra performance liquid chromatography-quadrupole time of flight mass spectrometry(UPLC-Q-TOF/MS)was established.Four representative diflorasone and esters(diflorasone,diflorasone diacetate,diflorasone-17-propionate,and diflorasone-21-propionate)were selected to optimize the pretreatment conditions,and 10 mL of extraction solvent dosage,15 min of extraction time and 5 g of salting-out agent as the optimal conditions were selected by response surface methodology.The results showed that the four analytes exhibited good linearity within the concentration range of 2.0?100 μg/L with the chromatographic peak area,and the correlation coefficients(R2)were all greater than 0.9990,while the results of recovery and relative standard deviation could satisfy the requirements of determination.The common characteristic ions of diflorasone and esters werem/z121 andm/z335,and their specific structures were obtained by analyzing the cleavage pathway based on the optimized determination conditions.A targeted screening method for other esters of diflorasone based on characteristic ions guidance strategy was established.This method had many advantages such as high efficiency,high sensitivity and good reproducibility,and could be used for targeted screening of diflorasone and esters in health products.The developed characteristic ion guided strategy could be employed to construct mass spectral databases for various glucocorticoids,enabling comprehensive targeted screening across a broad range of compounds.

Objective To analyze the prescription patterns of Professor Huang Feng,a nationally renowned traditional Chinese medicine(TCM)practitioner,in treating osteomyelitis using data mining methods.Methods Prescription data from effective medical records of osteomyelitis treated by Professor Huang Feng between January 2018 and December 2022 were collected and screened.Microsoft Excel,SPSS Modeler 18.0,and SPSS Statistics 25 were used to analyze the frequency and the distribution of properties,flavors,and meridian tropism of prescribed medications,along with association rule analysis and cluster analysis of high-frequency drugs.Results A total of 137 prescriptions involving 86 Chinese medicinals were included.Eighteen high-frequency medicinals(frequency＞30 times)were identified,namely Glycyrrhizae Radix et Rhizoma,Astragali Radix,Coicis Semen,Angelicae Sinensis Radix,Smilacis Glabrae Rhizoma,Achyranthis Bidentatae Radix,Bletillae Rhizoma,Rehmanniae Radix,Paeoniae Radix Alba,Dendrobii Caulis,Polygalae Radix,Lablab Semen Album,Corydalis Rhizoma,Angelicae Dahuricae Radix,Drynariae Rhizoma,Sanguisorbae Radix,Poria,and Mume Fructus.Most of the prescribed medicinals were neutral in nature,sweet,bitter,and pungent in flavor,and had the meridian tropism of liver,spleen,and lung meridians.Association rule analysis yielded 67 drug association rules,and the high-support combinations were the drug combinations of Astragali Radix respectively with Coicis Semen,Angelicae Sinensis Radix,Smilacis Glabrae Rhizoma and Achyranthis Bidentatae Radix,reflecting the compatibility principles of supplementing and invigorating qi-blood,activating blood circulation to resolve stasis,and draining dampness to remove toxins.Cluster analysis revealed three core clusters:Cluster 1 consisted of Glycyrrhizae Radix et Rhizoma,Astragali Radix,Coicis Semen,Smilacis Glabrae Rhizoma,Angelicae Sinensis Radix,Bletillae Rhizoma,Paeoniae Radix Alba,Angelicae Dahuricae Radix,Mume Fructus,Polygalae Radix and Sanguisorbae Radix;Cluster 2 consisted of Rehmanniae Radix and Dendrobii Caulis;Cluster 3 consisted of Achyranthis Bidentatae Radix,Lablab Semen Album,Corydalis Rhizoma and Poria.Conclusion For the treatment of osteomyelitis,Professor Huang Feng follows the principle of combining supporting healthy qi with eliminating pathogens,focuses on clearing damp-heat and pathogenic toxins accompanied by activating blood circulation to resolve stasis,and lays stress on adaptation to local condition and activating spleen-stomach to reinforce vital qi.

BACKGROUND: Hemorrhagic stroke (HS) is associated with significant disability and mortality. However, the relationship between meteorological factors and hemorrhagic stroke, as well as the potential moderating role of these factors, remains unclear. METHODS: Daily data on HS, air pollution, and meteorological conditions were collected from January 2015 to December 2021 in Ganzhou to analyze the relationship between meteorological factors and HS admissions. This analysis employed a time-stratified case-crossover design in conjunction with a distributional lag nonlinear model. Additionally, a bivariate response surface modelling was utilized to further investigate the interaction between meteorological factors and particulate matter. The study also stratified the analyses by gender and age. To investigate the potential impact of extreme weather conditions on HS, this study defined the 97.5th percentile as representing extremely high weather conditions, while the 2.5th percentile was classified as extremely low. RESULTS: In single-day lags, the risk of admissions for HS was significantly associated with extremely low temperature (lag 1-2 and lag 13-14), extremely low humidity (lag 1 and lag 9-12), and extremely high precipitation (lag 2-7). Females exhibited greater susceptibility to extremely low temperature than males within the single-day lag pattern in the subcomponent layer, with a maximum relative risk (RR) that was 7% higher. In the cumulative lag analysis, the risk of HS admissions was significantly associated with extremely high temperature (lag 0-8∼lag 0-14), extremely low humidity (lag 0-2∼lag 0-14), and extremely high precipitation (lag 0-4∼lag 0-14). Within the cumulative lag day structure of the subcomponent layer, both extremely low and extremely high temperature had a more pronounced effect on females and aged ≥65 years. The risk of HS admissions was positively associated with extremely high barometric pressure in the female subgroups (lag 0-1 and lag 0-2). The highest number of HS admissions occurred when high PM_2.5 concentrations coexisted with low precipitation. CONCLUSIONS Meteorological factors were significantly associated with the risk of hospital admissions for HS. Individuals who were female and aged ≥65 years were found to be more susceptible to these meteorological influences. Additionally, an interaction was observed between airborne particulate matter and meteorological factors. These findings contributed new evidence to the association between meteorological factors and HS.
China/epidemiology* ; Humans ; Female ; Male ; Aged ; Middle Aged ; Cross-Over Studies ; Hospitalization/statistics & numerical data* ; Adult ; Hemorrhagic Stroke/etiology* ; Meteorological Concepts ; Weather ; Particulate Matter/analysis* ; Air Pollution/adverse effects* ; Environmental Exposure/adverse effects* ; Aged, 80 and over ; Young Adult

Cervical spinal cord injury without fracture-dislocation (CSCIWFD) is referred to as a special type of cervical spinal cord injury characterized by traumatic spinal cord dysfunction and no significant bony structural abnormalities on imagines. Duo to the high risk of missed diagnosis during the initial consultation, CSCIWFD may lead to progressive neurological deterioration or even complete paralysis, severely impacting patients′ prognosis. Currently, there are no established consensuses over the diagnosis and treatment of CSCIWFD, such as the lack of evidence-based standards for indications of non-surgical treatment and risk of secondary neurological injury, as well as debates over the optimal timing for surgical intervention and indications for different surgical approaches. To address these issues, the Spine Trauma Group of the Orthopedic Branch of the Chinese Medical Doctor Association organized experts in the relevant fields to formulate Diagnosis and treatment guideline for acute cervical spinal cord injury without fracture- dislocation in adults ( version 2025) . Based on evidence-based medicine and the principles of scientific rigor and clinical applicability, the guidelines proposed 11 recommendations covering terminology, diagnosis, evaluation treatment, and rehabilitation, etc., aiming to standardize the management of CSCIWFD.