As an extension of the central nervous system, the retina is the only tissue in the body that allows direct in vivo observation of both neural and vascular structures, offering a unique non-invasive window for the assessment of cerebral neurovascular diseases. Recent studies have shown that multimodal imaging techniques—such as fundus photography, optical coherence tomography(OCT), and OCT angiography(OCTA)—enable quantitative analysis of retinal indicators including vascular caliber, fractal dimension, retinal nerve fiber layer thickness, and microvascular density. These metrics can, to some extent, reflect the severity and progression risk of cerebrovascular and neurodegenerative diseases such as stroke, Alzheimer's disease, and Parkinson's disease. Consequently, retinal imaging holds promise as a powerful tool for early screening, risk stratification, and treatment monitoring. This article systematically reviews the application of retinal imaging technologies in the diagnosis and management of cerebral neurovascular disorders, with a particular focus on the important role of artificial intelligence(AI)-driven image analysis in enhancing screening efficiency, improving risk assessment accuracy, and promoting multidisciplinary collaborative care, thereby providing insights for clinical translation.

ObjectiveTo study the changing law of appearance color and physicochemical properties of Angelicae Sinensis Radix Carbonisata（ASRC） during the processing by color space method combined with statistical analysis， so as to provide reference for determining the processing endpoint and evaluating the quality of the decoction pieces. MethodsTaking processing time（4， 8， 12， 16 min） and temperature（180， 200， 220， 240 ℃） as factors， ASRC decoction pieces with different processing degrees were prepared in a completely randomized design. Then， the brightness value（L^*）， red-green value（a^*）， yellow-blue value（b^*）， and total chromaticity value （E^*ab） of the decoction pieces were determined by spectrophotometer， the color difference value（ΔE） was calculated， and the data of colorimetric values were analyzed by discriminant analysis. At the same time， the pH， charcoal adsorption， and contents of tannins， 5-hydroxymethylfurfural（5-HMF）， tryptophan， chlorogenic acid， ferulic acid， senkyunolide I， senkyunolide H and ligustilide of ASRC with different processing degrees were determined by pH meter， ultraviolet and visible spectrophotometry and ultra-high performance liquid chromatography（UPLC）. Principal component analysis（PCA） was used to analyze the data of physicochemical indexes， after determining the processing technology of ASRC， the canonical discriminant function was established to distinguish the decoction pieces with different processing degrees， and leave-one-out cross validation was conducted. Finally， Pearson correlation analysis was used to explore the correlation between various physicochemical indexes and chromaticity values. ResultsWith the prolongation of the processing time， L^*， a^*， b^* and E^*ab all showed a decreasing trend， and the established discriminant model based on color parameters was able to distinguish ASRC with different processing degrees. The pH showed an increasing trend with the prolongation of processing time， and the charcoal adsorption， and the contents of tannins， 5-HMF， and tryptophan all showed an increasing and then decreasing trend. Among them， the charcoal adsorption， contents of tannin and 5-HMF reached their maximum values successively after processing for 8-12 min. While the contents of chlorogenic acid， ferulic acid， senkyunolide I， senkyunolide H and ligustilide decreased with the increase of processing time， with a decrease of 60%-80% at 8 min of processing. Therefore， the optimal processing time should be determined to be 8-12 min. PCA could clearly distinguish ASRC with different processing degrees， while temperature had no significant effect on the processing degree. The 12 batches of process validation results（10 min， 180-240 ℃） showed that except for 3 batches identified as class Ⅱ light charcoal， all other batches were identified as class Ⅲ standard charcoal， and the chromaticity values of each batch of ASRC were within the reference range of class Ⅱ-Ⅲ sample chromaticity values. The correlation analysis showed that the chromaticity values were negatively correlated with pH and charcoal adsorption， and positively correlated with contents of tryptophan， chlorogenic acid， ferulic acid， senkyunolide I， senkyunolide H， and ligustilide. And both pH and charcoal adsorption were negatively correlated with the contents of the above components， but the charcoal adsorption was positively correlated with the content of 5-HMF. ConclusionThe chromaticity values and the contents of various physicochemical indicators of ASRC undergo significant changes with the prolongation of processing time， and there is a general correlation between chromaticity values and various physicochemical indicators. Based on the changes in color and physicochemical indicators， the optimal processing time for ASRC is determined to be 8-12 min. This study reveals the dynamic changes of the relevant indexes in the processing of ASRC， which can provide a reference for the discrimination of the processing degree and the quantitative study of the processing endpoint.

Coronavirus disease 2019(COVID-19)is a respiratory syndrome caused by severe acute respiratory syn-drome coronavirus 2(SARS-CoV-2).Once SARS-CoV-2 was detected in Wuhan(Hubei Province,China),it rap-idly spread widely across China and the world,posing a serious threat to global health and the economy.With the development of Artificial Intelligence(AI)technology and the openness and application of open-source software,tremendous progress has been achieved in the quantitative study of chest radiology for COVID-19,enabling the quantitative dataization of radiological image data,which adds a powerful indicator to the prognosis study of COV-ID-19.Therefore,we reviewed the current literature on the factors predicting the severity and prognosis of COV-ID-19,and summarized demographic,laboratory and radiological factors to help with risk stratification and prog-nosis assessment for COVID-19 patients and assist in their clinical management and treatment.

Objective To investigate the correlation between platelet-to-albumin ratio(PAR)and occurrence of cerebral infarction after left atrial appendage closure(LAAC)in patients with non-valvular atrial fibrillation(NVAF).Methods A retrospective study was conducted on 259 NVAF patients undergoing LAAC in our department between 2019 and 2023.According to occurrence of cerebral infarction after LAAC or not,they were divided into a control group(241 cases)and a study group(18 cases).Their general data were collected,and Cox proportional hazards regression model was used to identify the risk factors for cerebral infarction.ROC curve was plotted to assess the predictive value of PAR for cerebral infarction in NVAF patients after LAAC,and the AUC value was calculated.Kaplan-Meier survival curve was drawn to analyze the incidence of cerebral infarction after LAAC in NVAF patients with different PAR values.Results The study group had significantly advanced age,higher SBP at admission,increased WBC,neutrophil,monocyte and platelet counts,longer thrombin time,elevated international normalized ratio(INR)and high-sensitivity C-reactive protein(hs-CRP)level,and higher PAR than the control group(P＜0.05,P＜0.01).Multivariate Cox regression analysis showed that PAR(HR=2.286,95％CI:1.182-4.420,P＜0.05)was an independent risk factor for cerebral infarction in NVAF patients after LAAC.ROC curve indicated that the AUC value of PAR in predicting cerebral infarction after LAAC in NVAF patients was 0.721(95％CI:0.586-0.856,P＜0.01),with an optimal cut-off value of 4.137,a sensitivity of 66.39％,and a specificity of 77.78％.Kaplan-Meier survival curve revealed that the higher the PAR value was,the higher the risk of cerebral infarction was(P＜0.01).Conclusion PAR is significantly correlated with cerebral infarction in NVAF patients after LAAC.The higher the PAR,the higher the risk of cerebral infarction,demonstrating its predictive value and being worthy of clinical promotion.

Very-late-onset schizophrenia-like psychosis(VLOSLP)is considered the most common mental disorder in the elderly following dementia and depression, and its clinical symptomatology is different from that of schizophrenia with onset around the average age.The diagnostic classification system and prognosis of VLOSLP are very complicated, and it is still controversial whether it should be classified as a prodromal symptom of dementia or as a type of late-onset schizophrenia.This article reviewed studies concerning the epidemiology, clinical features, diagnosis, prognosis, and neuroimaging of VLOSLP, hoping to provide value as a reference for future clinical research and treatment.

Lung cancer is one of the malignant tumours with the highest morbidity and mortality rates worldwide today, posing a major threat to human health. Accurate diagnosis and standardised treatment play a crucial role in improving the survival rate of lung cancer patients. In recent years, the rapid rise of artificial intelligence (AI) has brought about significant changes in the medical field, providing a new diagnostic and treatment model for lung cancer, and making a series of breakthroughs in lung cancer diagnostic imaging, pathological diagnosis, surgical oncology, radiotherapy, and drug development and treatment. This article introduces the current status of AI application in the field of lung cancer diagnosis and treatment, and extensively discusses the current challenges and future prospects, hoping to provide references and suggestions for future clinical practice.

Objective:To explore how one-sided/two-sided brain blood flow affects the occurrence of neurological complications in patients with Stanford type A aortic dissection, as well as to assess the factors that contribute to the development of neurological complications.Methods:A total of 162 patients diagnosed with Stanford type A aortic dissection who had undergone ascending aorta and total aortic arch replacement at Affiliated Hospital of Jining Medical College from August 2020 to December 2023 were retrospectively reviewed. These patients were categorized into two groups based on the presence of postoperative neurological complications: a group with neurological complications comprising 77 cases and a group without neurological complications comprising 85 cases. A comparative analysis was carried out on general clinical data, surgical and brain perfusion characteristics, as well as preoperative test indicators between these two groups in order to investigate the factors influencing the occurrence of postoperative neurological complications in patients with Stanford type A aortic dissection. The data was analyzed using Logistic regression to identify the risk factors associated with postoperative neurological complications and to develop a predictive nomogram model. Calibration curves, receiver operating characteristic (ROC) curves and decision curve (DCA) were generated to assess the accuracy and predictive capability of the nomogram model.Results:In the group of patients who experienced neurological complications, there was a higher prevalence of a history of hypertension, longer operation time, extended periods of cardiopulmonary bypass, cross-clamping, brain perfusion, cooling, and rewarming, as well as increased postoperative drainage volume. Additionally, the levels of preoperative blood urea nitrogen (BUN), creatinine (Cr) and lactic acid (Lac) were elevated compared to those in the non-neurological complications group: 77.9% (60/77) vs. 52.9% (45/85), (409.99 ± 104.26) min vs. (348.29 ± 63.12) min, (223.36 ± 66.86) min vs. (179.25 ± 38.59) min, 112 (94, 133) min vs. 96 (84, 113) min, (35.23 ± 9.89) min vs. (32.14 ± 6.81) min, (82.19 ± 28.69) min vs. (68.76 ± 29.06) min, (79.30 ± 22.60) min vs. (69.54 ± 16.42) min, 806 (529, 1 127) ml vs. 663 (449, 925) ml, 6.78 (5.38, 8.84) mmol/L vs. 6.08 (4.66, 7.76) mmol/L, 86.3 (64.0, 131.9) μmol/L vs. 71.0 (55.6, 84.9) μmol/L, 2.1(1.2, 4.0) mmol/L vs. 1.5 (0.9, 2.3) mmol/L. On the other hand, the percentage of patients who underwent bilateral brain perfusion was lower, and they experienced lower lowest temperature, preoperative platelet count, and ejection fraction levels than those in the non-neurological complications group: 57.1% (44/77) vs. 75.3% (64/85), (25.69 ± 1.04) ℃ vs. (26.04 ± 0.82) ℃, (175.79 ± 58.14) ×10 9/L vs. (213.87 ± 77.29) ×10 9/L, (54.18 ± 3.84)% vs. (55.34 ± 3.56)% ( P<0.05). Multivariate Logistic regression analysis revealed that a prior history of high blood pressure, prolonged cardiopulmonary bypass duration were identified as autonomous risk factors for the development of postoperative neurological issues in individuals with Stanford type A aortic dissection, while simultaneous brain perfusion emerged as an independent protective element ( P<0.05). Subsequently, a predictive nomogram was constructed incorporating these three pivotal factors to assess the likelihood of postoperative neurological complications in patients with Stanford type A aortic dissection. The calibration curve exhibited a noteworthy level of accuracy for the nomogram predictive model ( χ2 = 9.01, P = 0.342). Additionally, the ROC curve analysis displayed an area under the curve of 0.84 (95% CI 0.78 to 0.90) for the nomogram model in predicting postoperative neurological complications in patients with Stanford type A aortic dissection, indicating a high predictive accuracy. Moreover, DCA analysis indicated that the nomogram model provided a net benefit above 0 across the spectrum of 0 to 90%. Conclusions:Postoperative neurological complications in patients with Stanford type A aortic dissection is linked to factors such as a previous history of hypertension, unilateral brain perfusion, an extended cardiopulmonary bypass duration. By developing a nomogram model that incorporates these factors, it becomes feasible to accurately forecast the likelihood of postoperative neurological complications in this patient population. This predictive tool holds significant value in facilitating proactive clinical risk evaluation and preventive measures.

Objective In this study,we investigated the correlation between the scores for different Liebowitz Social Anxiety Scale(LSAS)subscale models and the metabolic activity in specific regions of the brain using 18F-fluorodeoxyglucose(FDG)positron emission tomography/computed tomography(PET/CT)(18F-FDG PET),thereby improving the understanding of the neurobiological characteristics of social anxiety.Methods A total of 39 cognitively normal participants(29 men and 10 women,aged 30-63 years)were enrolled.All participants underwent LSAS assessment and brain 18F-FDG PET scanning.Correlations between metabolic activities in various brain regions and scores from the different LSAS subscales were analyzed accordingly.Results LSAS subscale scores were significantly correlated with metabolic activity in specific brain regions.In the Safren model,the score for the observation by others subscale was positively correlated with the left fusiform gyrus(P<0.001,false discovery rate[FDR]-corrected)and the left caudate tail(P<0.001,FDR-corrected),suggesting a close association between mood states related to observation by others and the metabolic activity in these regions.In the Baker model,the score for the eating and drinking subscale was negatively correlated with the right precuneus(P<0.001,FDR-corrected),while the score for the assertiveness subscale was positively correlated with the left caudate nucleus(P<0.001,FDR-corrected).These findings revealed the complex associative patterns between various mood and behavioral dimensions and metabolic activities in specific brain regions.Conclusion Social anxiety symptoms are closely associated with metabolic changes in specific brain regions,including the left insula,left caudate tail,and right precuneus.Moreover,different social situations activate distinct brain regions.Compared with individuals with social anxiety disorder,normal individuals exhibit involvement of fewer brain regions when experiencing social anxiety.These findings provide new experimental evidence for understanding the neural mechanisms underlying social anxiety.

Aim To investigate the effect of long-chain non-coding RNA(lncRNA)GS1-124K5.4 targeting regulation of PRDX6 on proliferation,migration and in-vasion of lung squamous carcinoma(LUSC)cells and the underlying mechanism.Methods The expression level of lncRNA GS1-124K5.4 in lung cancer tissues and adjacent tissues of 60 patients with LUSC were de-termined by fluorescence in situ hybridization.The ex-pression level of lncRNA GS1-124K5.4 in human nor-mal lung cells and LUSC cells were determined by qRT-PCR.Two kinds of LUSC cells(NCI-H 1703,SK-MES-1)with highest expression level of lncRNA GS1-124K5.4 were selected for subsequent experi-ments.The distribution of lncRNA GS1-124K5.4 in cells was studied by fluorescence in situ hybridization and prokaryotic separation.The effect of knockdown of lncRNA GS1-124K5.4 on proliferation of NCI-H1703 and SK-MES-1 cells was studied by CCK-8 experiment and cell clone formation experiment;the effect of knockdown of lncRNA GS1-124K5.4 on migration of NCI-H1703 and SK-MES-1 cells was studied by cell scratch experiment and Transwell cell migration experi-ment;and the effect of knockdown of lncRNA GS1-124K5.4 on invasion of NCI-H1703 and SK-MES-1 cells was studied by Transwell invasion experiment.The protein to be bound by lncRNA GS1-124K5.4 was detected by RNA pull-down combined with mass spec-trometry and immune-precipitation.The effect of knockdown of lncRNA GS1-124K5.4 targeting PRDX6 on proliferation,migration and invasion of NCI-H1703 and SK-MES-1 cells was studied.Results(1)The fluorescence intensity of lncRNA GS1-124K5.4 in lung squamous cell carcinoma increased compared with that in adjacent tissues(P＜0.05),and the expression of lncRNA GS1-124K5.4 was related with lymph node metastasis and clinical stage(P＜0.05).(2)The ex-pression level of lncRNA GS1-124K5.4 in NCI-H1703,NCI-H520 and SK-MES-1 cells significantly increased(P＜0.05).(3)The result of fluorescence in situ hybridization experiment and nucleoplasm sepa-ration experiment showed that lncRNA GS1-124K5.4 was mainly distributed in cell nucleus.(4)The prolif-eration,migration and invasion ability of NCI-H1703 and SK-MES-1 cells with knockdown of lncRNA GS1-124K5.4 significantly decreased(P＜0.05).(5)PRDX6 protein to be bound to LncRNA GS1-124K5.4 was determined by RNA pull-down combined with mass spectrometry and immunoprecipitation.(6)The prolif-eration,migration and invasion ability of NCI-H1703 and SK-MES-1 cells with overexpression of lncRNA GS1-124K5.4 significantly increased(P＜0.05);the proliferation,migration and invasion ability of NCI-H1703 and SK-MES-1 cells with knockdown of PRDX6 significantly decreased(P＜0.05);the proliferation,migration and invasion ability of NCI-H1703 and SK-MES-1 cells with overexpression of lncRNAGS1-124K5.4 and knockdown of PRDX6 showed no signifi-cant change(P＞0.05).Conclusions LncRNA GS1-124K5.4 is highly expressed in lung squamous cell carcinoma,and it may promote the proliferation,migration and invasion of lung squamous carcinoma cells by targeting the expression of PRDX6 protein.

Lung cancer is one of the malignant tumours with the highest morbidity and mortality rates worldwide today, posing a major threat to human health. Accurate diagnosis and standardised treatment play a crucial role in improving the survival rate of lung cancer patients. In recent years, the rapid rise of artificial intelligence (AI) has brought about significant changes in the medical field, providing a new diagnostic and treatment model for lung cancer, and making a series of breakthroughs in lung cancer diagnostic imaging, pathological diagnosis, surgical oncology, radiotherapy, and drug development and treatment. This article introduces the current status of AI application in the field of lung cancer diagnosis and treatment, and extensively discusses the current challenges and future prospects, hoping to provide references and suggestions for future clinical practice.