ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

OBJECTIVE To analyze the characteristics of imported malaria cases in West China Hospital of Sichuan University in recent years and to provide reference for the prevention and control of imported infectious diseases.METHOD A retrospective analysis of 62 cases of imported malaria from abroad reported in West China Hospital of Sichuan University from 2012 to 2023 were retrospectively analyzed.RESULTS From 2012 to 2023,62 cases of imported malaria were reported,including 49 cases(79.03％)of falciparum malaria,10 cases(16.13％)of vivax malaria,and 3 cases(4.84％)of ovale malaria.Among the imported malaria cases,9 cases were severe malaria,with 8(16.33％,8/49)severe cases caused by falciparum malaria,of which 6 cases(75.00％,6/8)were cere-bral malaria.The cases were mainly Chinese citizens and young-to-middle-aged adults,with the highest concentra-tion in the 40-49 age group(37.10％,23/62).There were more males than females,with a male-to-female sex ratio of 11.4∶1;the predominant occupation was worker(38.71％,24/62).The primary region of importation was Africa(90.32％,56/62).There was importation throughout the year,with no distinct seasonal distribution pattern.Two of the admitted cases died(severe cases of falciparum malaria,which developed into cerebral malari-a),while the rest were improved and discharged from the hospital after standardized treatment.CONCLUSIONS Cases of imported malaria from abroad are characterized by Chinese nationality,males,young adults and workers.The type of malaria is mainly falciparum malaria,and the prognosis for most cases is relatively good.It is necessary to strengthen the construction of joint prevention and control systems and other long-term mechanisms,and to continuously and scientifically implement various strategies and measures to prevent the re-emergence of malaria through imported ca-ses,in order to avoid the occurrence of secondary cases resulting from imported infections.

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 analyze the characteristics of imported malaria cases in West China Hospital of Sichuan University in recent years and to provide reference for the prevention and control of imported infectious diseases.METHOD A retrospective analysis of 62 cases of imported malaria from abroad reported in West China Hospital of Sichuan University from 2012 to 2023 were retrospectively analyzed.RESULTS From 2012 to 2023,62 cases of imported malaria were reported,including 49 cases(79.03％)of falciparum malaria,10 cases(16.13％)of vivax malaria,and 3 cases(4.84％)of ovale malaria.Among the imported malaria cases,9 cases were severe malaria,with 8(16.33％,8/49)severe cases caused by falciparum malaria,of which 6 cases(75.00％,6/8)were cere-bral malaria.The cases were mainly Chinese citizens and young-to-middle-aged adults,with the highest concentra-tion in the 40-49 age group(37.10％,23/62).There were more males than females,with a male-to-female sex ratio of 11.4∶1;the predominant occupation was worker(38.71％,24/62).The primary region of importation was Africa(90.32％,56/62).There was importation throughout the year,with no distinct seasonal distribution pattern.Two of the admitted cases died(severe cases of falciparum malaria,which developed into cerebral malari-a),while the rest were improved and discharged from the hospital after standardized treatment.CONCLUSIONS Cases of imported malaria from abroad are characterized by Chinese nationality,males,young adults and workers.The type of malaria is mainly falciparum malaria,and the prognosis for most cases is relatively good.It is necessary to strengthen the construction of joint prevention and control systems and other long-term mechanisms,and to continuously and scientifically implement various strategies and measures to prevent the re-emergence of malaria through imported ca-ses,in order to avoid the occurrence of secondary cases resulting from imported infections.

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 aim of this study was to qualitatively and quantitatively detect coronavirus(CoV)in the feces of bats from Qinghua Cave,Yunnan Province,China.CoV was qualitatively tested with reverse transcription polymerase chain reaction(RT-PCR),and homology and genetic evolution were analyzed with bioinformatics software.The established reverse transcription real-time fluores-cence quantitative PCR(qRT-PCR)method was applied to CoV quantification in bat feces.The positivity rate of CoV in 306 fecal samples collected from the fulvous fruit bat(Rousettus leschenaultia)was 7.8%(24/306)according to RT-PCR.All 24 strains of CoV belonged to β-CoV,and showed a similarity of 86.8%-100.0%at the nucleotide level and 95.2%-100.0%at the amino acid level,with respect to other β-CoV sequences in the NCBI database.The positivity rate of CoV was 18.6%(57/306)according to qRT-PCR,a value higher than that according to RT-PCR(χ2=25.3,P<0.05).The mean β-CoV load was 1.3×103 copies/μL.In conclusion,the bats in Qinghua Cave,Yunnan Province,carried CoV belonging to β-CoV.The established qRT-PCR method achieved good sensitiv-ity,accuracy,reproducibility,and a higher detection rate than that of RT-PCR,and can be used for rapid detection of β-CoV in bats.

Objective:To explore the therapeutic effect of drug-coated balloon(DCB)combined drug-eluting stent(DES)on coronary bifurcation lesions.Methods:A total of 108 patients with coronary bifurcation lesions admitted in Shanghai Ninth People's Hospital Huangpu Branch,Shanghai Jiaotong University School of Medicine between February 2021 and March 2023 were enrolled in this randomized controlled study.Patients were randomly divided into combined treatment group(n=54,DCB was implanted in the sub-branch,and DES was implanted in the main branch)and DES group(n=54,DES were implanted in both main branch and sub-branch).Clinical therapeutic effect,coronary angiography quantitative parameters before,instant and 9 months after operation and clinical out-comes during 1-year follow-up after operation were compared between two groups.Results:The total effective rate of combined treatment group was significantly higher than that of DES group(96.30％vs.87.04％,P=0.030).Compared to those in DES group,instant and 9 months after operation,patients in the combined treatment group had significant higher diameters of main branch vessel[(3.13±0.31)mm vs.(3.01±0.25)mm,(2.99±0.33)mm vs.(2.84±0.23)mm],sub-branch vessel[(2.51±0.26)mm vs.(2.42±0.13)mm,(2.44±0.24)mm vs.(2.29±0.36)mm],and significant lower main branch stenosis rate[(6.05±0.21)％vs.(6.24±0.31)％,(9.06±0.23)％vs.(10.12±0.12)％]and sub-branch stenosis rate[(7.38±0.42)％vs.(7.63±0.18)％,(8.07±0.39)％vs.(11.25±0.22)％](P＜0.05 or＜0.01).There were no significant difference in incidence of target lesion revascularization,cardiogenic death and major adverse cardiovascular events between two groups(P＞0.05 all).Conclusion:Drug-eluting stent combined drug-coated balloon may promote vascular branch dilation of coro-nary artery lesions,increase the minimum lumen diameter of sub-branch vessels,and reduce the occurrence of ste-nosis in the treatment of coronary bifurcation lesion,which had similar effectiveness and safety with drug-eluting stent technique.

Objective:To explore the therapeutic effect of drug-coated balloon(DCB)combined drug-eluting stent(DES)on coronary bifurcation lesions.Methods:A total of 108 patients with coronary bifurcation lesions admitted in Shanghai Ninth People's Hospital Huangpu Branch,Shanghai Jiaotong University School of Medicine between February 2021 and March 2023 were enrolled in this randomized controlled study.Patients were randomly divided into combined treatment group(n=54,DCB was implanted in the sub-branch,and DES was implanted in the main branch)and DES group(n=54,DES were implanted in both main branch and sub-branch).Clinical therapeutic effect,coronary angiography quantitative parameters before,instant and 9 months after operation and clinical out-comes during 1-year follow-up after operation were compared between two groups.Results:The total effective rate of combined treatment group was significantly higher than that of DES group(96.30％vs.87.04％,P=0.030).Compared to those in DES group,instant and 9 months after operation,patients in the combined treatment group had significant higher diameters of main branch vessel[(3.13±0.31)mm vs.(3.01±0.25)mm,(2.99±0.33)mm vs.(2.84±0.23)mm],sub-branch vessel[(2.51±0.26)mm vs.(2.42±0.13)mm,(2.44±0.24)mm vs.(2.29±0.36)mm],and significant lower main branch stenosis rate[(6.05±0.21)％vs.(6.24±0.31)％,(9.06±0.23)％vs.(10.12±0.12)％]and sub-branch stenosis rate[(7.38±0.42)％vs.(7.63±0.18)％,(8.07±0.39)％vs.(11.25±0.22)％](P＜0.05 or＜0.01).There were no significant difference in incidence of target lesion revascularization,cardiogenic death and major adverse cardiovascular events between two groups(P＞0.05 all).Conclusion:Drug-eluting stent combined drug-coated balloon may promote vascular branch dilation of coro-nary artery lesions,increase the minimum lumen diameter of sub-branch vessels,and reduce the occurrence of ste-nosis in the treatment of coronary bifurcation lesion,which had similar effectiveness and safety with drug-eluting stent technique.

Objective To explore the improvement effect of ultrasound microbubble blasting assisted bone marrow mesenchymal stem cells(BMSC)transplantation on diabetic nephropathy(DN)in terms of inflammatory response and renal function via the Toll like receptor 4(TLR-4)/nuclear factor-kappa B(NF-κB)signaling pathway,as well as its specific mechanism.Methods A rat DN model was estab-lished and randomly grouped into the following:Model group,BMSC group,BMSC+microbubble group,and BMSC+microbubble+TLR-4/NF-κB pathway activator lipopolysaccharide(LPS)group,with 12 rats in each group.Additionally,12 rats were selected as the control(CK)group.Biochemical and biuret analysis,HE and Masson staining,ELISA,and Western blotting testing were employed to detect fasting blood glucose(FBG),total cholesterol(TC),triglyceride(TG),serum creatinine(sCr),blood urea nitrogen(BUN),24-hour urinary total protein(24 h-UTP),pathological changes in renal tissue,fibrosis status,and the expression of interleukin-1 β(IL-1β),tumor necrosis factorα(TNF-α),TLR-4,and NF-κB proteins of rats in each group.Results The renal tissue in the CK group was structurally normal with few collagen fibers.The Model group showed obvious renal tissue lesions and severe collagen fiber deposition;the lesions in the BMSC group and BMSC+microvesicle group were alleviated in turn,while the lesions were aggravated after the addition of LPS compared with the BMSC+microvesicle group.Compared with the CK group,the levels of FBG,TC,TG,sCr,BUN,24 h-UTP,as well as the expression of IL-1β,TNF-α,TLR-4,and NF-κB proteins were increased in the Model group(P＜0.05).Compared with the Model group,the above-men-tioned indices and protein expression were decreased in the BMSC group(P＜0.05).Compared with the BMSC group,the above-men-tioned indices and protein expression were further decreased in the BMSC+microvesicle group(P＜0.05).Compared with the BMSC+mi-crovesicle group,the above-mentioned indices and protein expression were increased in the BMSC+microvesicle+LPS group(P＜0.05).Conclusion The improvement effect of ultrasound microbubble blasting-assisted BMSC transplantation on DN rats may be related to the inhibition of the TLR-4/NF-κB signaling pathway.