BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

ObjectiveFunctional near-infrared spectroscopy (fNIRS), a novel non-invasive technique for monitoring cerebral activity, can be integrated with upper limb rehabilitation robots to facilitate the real-time assessment of neurological rehabilitation outcomes. The rehabilitation robot is designed with 3 training modes: passive, active, and resistance. Among these, the resistance mode has been demonstrated to yield superior rehabilitative outcomes for patients with a certain level of muscle strength. The control modes in the resistance mode can be categorized into dynamic and static control. However, the effects of different control modes in the resistance mode on the motor function of patients with upper limb hemiplegia in stroke remain unclear. Furthermore, the effects of force, an important parameter of different control modes, on the activation of brain regions have rarely been reported. This study investigates the effects of dynamic and static resistance modes under varying resistance levels on cerebral functional alterations during motor rehabilitation in post-stroke patients. MethodsA cohort of 20 stroke patients with upper limb dysfunction was enrolled in the study, completing preparatory adaptive training followed by 3 intensity-level tasks across 2 motor paradigms. The bilateral prefrontal cortices (PFC), bilateral primary motor cortices (M1), bilateral primary somatosensory cortices (S1), and bilateral premotor and supplementary motor cortices (PM) were examined in both the resting and motor training states. The lateralization index (LI), phase locking value (PLV), network metrics were employed to examine cortical activation patterns and topological properties of brain connectivity. ResultsThe data indicated that both dynamic and static modes resulted in significantly greater activation of the contralateral M1 area and the ipsilateral PM area when compared to the resting state. The static patterns demonstrated a more pronounced activation in the contralateral M1 in comparison to the dynamic patterns. The results of brain network analysis revealed significant differences between the dynamic and resting states in the contralateral PFC area and contralateral M1 area (F=4.709, P=0.038), as well as in the contralateral PM area and ipsilateral M1 area (F=4.218, P=0.049). Moreover, the findings indicated a positive correlation between the activation of the M1 region and the increase in force in the dynamic mode, which was reversed in the static mode. ConclusionBoth dynamic and static resistance training modes have been demonstrated to activate the corresponding brain functional regions. Dynamic resistance modes elicit greater oxygen changes and connectivity to the region of interest (ROI) than static resistance modes. Furthermore, the effects of increasing force differ between the two modes. In patients who have suffered a stroke, dynamic modes may have a more pronounced effect on the activation of exercise-related functional brain regions.

BACKGROUND: The primary limitation to kidney transplantation is organ shortage. Recent progress in gene editing and immunosuppressive regimens has made xenotransplantation with porcine organs a possibility. However, evidence in pig-to-human xenotransplantation remains scarce, and antibody-mediated rejection (AMR) is a major obstacle to clinical applications of xenotransplantation. METHODS: We conducted a kidney xenotransplantation in a brain-dead human recipient using a porcine kidney with five gene edits (5GE) on March 25, 2024 at Xijing Hospital, China. Clinical-grade immunosuppressive regimens were employed, and the observation period lasted 22 days. We collected and analyzed the xenograft function, ultrasound findings, sequential protocol biopsies, and immune surveillance of the recipient during the observation. RESULTS: The combination of 5GE in the porcine kidney and clinical-grade immunosuppressive regimens prevented hyperacute rejection. The xenograft kidney underwent delayed graft function in the first week, but urine output increased later and the single xenograft kidney maintained electrolyte and pH homeostasis from postoperative day (POD) 12 to 19. We observed AMR at 24 h post-transplantation, due to the presence of pre-existing anti-porcine antibodies and cytotoxicity before transplantation; this AMR persisted throughout the observation period. Plasma exchange and intravenous immunoglobulin treatment mitigated the AMR. We observed activation of latent porcine cytomegalovirus toward the end of the study, which might have contributed to coagulation disorder in the recipient. CONCLUSIONS 5GE and clinical-grade immunosuppressive regimens were sufficient to prevent hyperacute rejection during pig-to-human kidney xenotransplantation. Pre-existing anti-porcine antibodies predisposed the xenograft to AMR. Plasma exchange and intravenous immunoglobulin were safe and effective in the treatment of AMR after kidney xenotransplantation.
Transplantation, Heterologous/methods* ; Kidney Transplantation/methods* ; Heterografts/pathology* ; Immunoglobulins, Intravenous/administration & dosage* ; Graft Survival/immunology* ; Humans ; Animals ; Sus scrofa ; Graft Rejection/prevention & control* ; Kidney/pathology* ; Gene Editing ; Species Specificity ; Immunosuppression Therapy/methods* ; Plasma Exchange ; Brain Death ; Biopsy ; Male ; Aged

Medical institutions, with their clinical practice foundation and abundant human use experience data, have become important carriers for the inheritance and innovation of traditional Chinese medicine(TCM) and the "cradles" of the preparation of new TCM. To effectively promote the transformation of new TCM originating from the TCM clinical practice in medical institutions and establish an effective evaluation index system for the transformation of new TCM conforming to the characteristics of TCM, consensus experts adopted the literature research, questionnaire survey, Delphi method, etc. By focusing on the policy and technical evaluation of new TCM originating from the TCM clinical practice in medical institutions, a comprehensive evaluation from the dimensions of drug safety, efficacy, feasibility, and characteristic advantages was conducted, thus forming a comprehensive evaluation system with four primary indicators and 37 secondary indicators. The expert consensus reached aims to encourage medical institutions at all levels to continuously improve the high-quality research and development and transformation of new TCM originating from the TCM clinical practice in medical institutions and targeted at clinical needs, so as to provide a decision-making basis for the preparation, selection, cultivation, and transformation of new TCM for medical institutions, improve the development efficiency of new TCM, and precisely respond to the public medication needs.
Medicine, Chinese Traditional/standards* ; Humans ; Consensus ; Drugs, Chinese Herbal/therapeutic use* ; Surveys and Questionnaires

The study investigated the intrinsic changes in material basis of Angelicae Sinensis Radix during wine processing by headspace-gas chromatography-ion mobility spectrometry(HS-GC-IMS), headspace-solid phase microextraction-gas chromatography-mass spectrometry(HS-SPME-GC-MS), and ultra-high performance liquid chromatography-quadrupole-orbitrap mass spectrometry(UPLC-Q-Orbitrap-MS) combined with chemometrics. HS-GC-IMS fingerprints of Angelicae Sinensis Radix before and after wine processing were established to analyze the variation trends of volatile components and characterize volatile small-molecule substances before and after processing. Principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) were employed for differentiation and difference analysis. A total of 89 volatile components in Angelicae Sinensis Radix were identified by HS-GC-IMS, including 14 unsaturated hydrocarbons, 16 aldehydes, 13 ketones, 9 alcohols, 16 esters, 6 organic acids, and 15 other compounds. HS-SPME-GC-MS detected 118 volatile components, comprising 42 unsaturated hydrocarbons, 11 aromatic compounds, 30 alcohols, 8 alkanes, 6 organic acids, 4 ketones, 7 aldehydes, 5 esters, and 5 other volatile compounds. UPLC-Q-Orbitrap-MS identified 76 non-volatile compounds. PCA revealed distinct clusters of raw and wine-processed Angelicae Sinensis Radix samples across the three detection methods. Both PCA and OPLS-DA effectively discriminated between the two groups, and 145 compounds(VIP>1) were identified as critical markers for evaluating processing quality, including 4-methyl-3-penten-2-one, ethyl 2-methylpentanoate, and 2,4-dimethyl-1,3-dioxolane detected by HS-GC-IMS, angelic acid, β-pinene, and germacrene B detected by HS-SPME-GC-MS, and L-tryptophan, licoricone, and angenomalin detected by UPLC-Q-Orbitrap-MS. In conclusion, the integration of the three detection methods with chemometrics elucidates the differences in the chemical material basis between raw and wine-processed Angelicae Sinensis Radix, providing a scientific foundation for understanding the processing mechanisms and clinical applications of wine-processed Angelicae Sinensis Radix.
Wine/analysis* ; Gas Chromatography-Mass Spectrometry/methods* ; Chromatography, High Pressure Liquid/methods* ; Angelica sinensis/chemistry* ; Solid Phase Microextraction/methods* ; Drugs, Chinese Herbal/isolation & purification* ; Chemometrics ; Volatile Organic Compounds/chemistry* ; Principal Component Analysis ; Ion Mobility Spectrometry/methods*

Objective:To investigate the relationship between the severity of pharyngolaryngeal sensory impairment and swallowing biomechanics as well as the risk of penetration-aspiration in patients with dysphagia following infratentorial stroke.Methods:This retrospective cross-sectional study enrolled 51 patients with dysphagia following infratentorial stroke hospitalized in the Department of Rehabilitation Medicine of The Third Affiliated Hospital of Sun Yat-sen University between January 2022 and December 2023. Participants were categorized into three groups: normal sensation group [15 males, 2 females; age range 29-76 (56.0±13.3)years], diminished sensation group[16 males, 3 females; age range 38-80(62.0±11.8)years], and absent sensation group [14 males, 1 female; age range 44-75 (60.0±9.7)years]. All patients underwent laryngoscopy and videofluoroscopic swallowing study, which included pharyngolaryngeal sensory testing and Penetration-Aspiration Scale assessment. Swallowing temporal parameters were quantitatively analyzed. Group comparisons for different variable types were conducted using the Chi-square test, one-way ANOVA, and the Kruskal-Wallis test. The correlation between sensory groups and Penetration-Aspiration Scale scores was assessed using Spearman′s correlation analysis. Logistic regression was employed to analyze the impact of pharyngolaryngeal sensory function on penetration-aspiration events.Results:Among the 51 patients, 33.33% (17/51) had normal pharyngolaryngeal sensation, while, 66.67% (34/51) exhibited sensory impairment. The normal sensation group exhibited a significantly longer laryngeal vestibule closure (LVC) time [792 (643, 1 205) ms] compared to the diminished [528 (380, 776) ms] and absent sensation groups [380 (322, 404) ms] ( H=6.502, P=0.039). Additionally, the upper esophageal sphincter opening time was longer in the normal sensation group than in the absent sensation group [528 (371, 710) ms vs 182 (0, 710) ms, H=6.003, P=0.049]. Correlation analysis indicated a significant negative correlation between the severity of sensory impairment and Penetration-Aspiration Scale scores ( r=-0.366, P=0.008). Logistic regression analysis demonstrated that greater sensory impairment was an independent risk factor for penetration-aspiration ( OR=9.29, 95%CI=1.57-54.77, P=0.014). Conclusion:Pharyngolaryngeal sensory deficits are common after infratentorial stroke dysphagia and are significantly associated with impaired swallowing biomechanics and increased aspiration risk. The severity of sensory deficit is a key determinant of penetration-aspiration risk, highlighting its value in risk stratification and therapeutic decision-making for dysphagia.

Diagnosis and treatment of digestive tract tumors is not optimistic with high incidence and mortality.Early diagnosis is conducive to improving the survival rate and quality of life of the patients.A variety of endoscopic imaging techniques have been applied in the diagnosis of digestive tract tumors,each with its own advantages and disadvantages.Optical coherence tomography(OCT)has emerged as a non-invasive and high-resolution imaging technique with unique advantages in staging and diagnosis of superficial digestive tract tumors.The complexity of massive image processing has also been effectively addressed with the development of artificial intelligence(AI),and AI-assisted OCT imaging,especially in the diagnosis of digestive tract tumors,has shown good prospects.This review summarizes the principle and development of OCT,and discusses the potential of AI-assisted OCT for deep learning in the diagnosis of digestive tract tumors.

Objective:To investigate the impact of early invasive arterial blood pressure (IBP) monitoring on survival and neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients undergoing extracorporeal cardiopulmonary resuscitation (ECPR).Methods:This retrospective cohort study analyzed 44 OHCA patients receiving ECPR between January 2021 and January 2023. Patients were divided into: Early intervention group : IBP established within 3 min of ECMO initiation; Late intervention group : IBP established after ICU admission. Baseline characteristics, ECMO parameters, and clinical outcomes were compared. Multivariable logistic regression (adjusted for age, initial rhythm, etc.) and Spearman's correlation were used.Results:This study included a total of 44 patients treated with OHCA and ECPR, divided into an early intervention group of 23 cases and a late intervention group of 21 cases. The early intervention group showed significantly higher: Survival to discharge (43.5% vs. 9.5%, P<0.05), Good neurological recovery (CPC 1-2: 34.8% vs. 9.5%, P<0.05).Early intervention independently predicted survival (adjusted OR=18.84, 95% CI:1.97-179.98, P=0.01). Stratified analysis by pH (cutoff 7.0) demonstrated consistent benefits in both pH>7.0 ( aOR=0.392, 95% CI:0.106-0.678) and pH≤7.0 subgroups ( aOR=0.385, 95% CI: 0.075-0.695; interaction P=0.183). Early IBP positively correlated with CPC scores ( ρ=0.40, P=0.007). Conclusions:Early IBP monitoring significantly improves survival and neurological outcomes in OHCA-ECPR patients, supporting its integration into standardized protocols.

Objective To investigate the effectiveness and safety of infarct core resection combined with decompressive craniectomy(DC)based on corticospinal tract(CST)protection in the treatment of massive cerebral infarction(MCI)with malignant brain edema.Methods This study retrospectively enrolled MCI patients with malignant brain edema who underwent internal decompression combined with DC at Xingtai Central Hospital from January 2021 to June 2024.The enrolled patients were divided into a control group and an experimental group base on the intracranial internal decompression method used.All patients underwent CT perfusion(CTP),CT angiography(CTA),diffusion-weighted imaging(DWI),and diffusion tensor imaging(DTI)within 24 h of admission.Preoperative imaging data was automatically processed using an artificial intelligence diagnostic system.For the experimental group,the imaging data was fused within a neuro-navigation system preoperatively to visualize the spatial relationships between the infarct core,ischemic penumbra,and CST and infarct core resection combined with DC was performed while protecting the CST through neuro-navigation.The control group underwent anterior temporal lobectomy combined with DC.Baseline and clinical data were collected from both groups,including gender,age,smoking history,alcohol consumption history,diabetes,hypertension,hyperlipidemia,hyperhomocysteinemia,atrial fibrillation history,responsible occluded vessel(internal carotid artery,middle cerebral artery),preoperative infarct volume on DWI,preoperative ischemic penumbra volume,preoperative the National Institutes of Health stroke scale(NIHSS)score,time from onset to surgery,intraoperative procedure duration,intraoperative blood loss,preoperative and 1-month postoperative fraction anisotropy(FA)values of the CST on the affected side,modified Rankin scale(mRS)score at 6 months postoperatively,and surgery-related complications within 1 month postoperatively(intracranial hemorrhage[operative site oozing,hemorrhagic transformation]and intracranial infection[surgical incision site infection,empyema,brain abscess,meningitis]).6-month follow-up after surgery were conducted through outpatient visit or telephone calls and prognosis of patients was evaluated using the mRS(with mRS of 0-3 defined as good prognosis,4-6 as poor prognosis,and 6 indicating death).The effectiveness indicators included FA value of the affected CST at 1 month postoperatively,good prognosis rate after surgery at 6 months,and 6-month mortality rate after surgery.The safety indicators included the incidence rates of surgical complications(intracranial hemorrhage and infection)within 1 month postoperatively.Based on preoperative DTI images,all patients were further divided into a CST-intact(infarct core did not invade CST,CST morphology intact or deformed/shifted)and a CST-damaged(infarct core invaded CST,CST disrupted or interrupted)subgroup for analysis.Results A total of 62patients(37 males,25 females,age 49-60 years,mean[55±4]years)were enrolled in this study.With 28 patients in the experimental group and 34 in the control group.(1)No significant differences were found in baseline or clinical data between the experimental and control groups(all P＞0.05),and the reoperative FA values of the affected CST were showed no significant differences(P=0.588).(2)The efficacy and safety metrics were evaluated.For the efficacy indices,at 1 month after the surgery,FA values of the affected CST increased significantly compared to preoperative values in both groups(0.409±0.051 vs.0.312±0.052 in the experimental group,and,0.381±0.048 vs.0.319±0.049 in control group;both P＜0.05),and the FA value was significantly higher in the experimental group than that in the control group(0.409±0.051 vs.0.381±0.048,P=0.030).At the 6-month follow-ups,the good prognosis rate was significantly higher in the experimental group than that in the control group(39.3％[11/28]vs.14.7％[5/34],P=0.028).No significant difference in the 6-month mortality rate were observed between the two groups(P=0.787).For the safety indices,no significant differences were found in the incidence rates of intracranial hemorrhage or intracranial infection within 1 month postoperatively between the two groups(both P＞0.05).(3)For further subgroup analysis,no significant differences were found in baseline or clinical data between the CST-damaged subgroup and the CST-intact subgroup in both the experimental and control groups(all P＞0.05).In CST-intact subgroup,FA values of the affected CST increased significantly at 1 month postoperatively compared to preoperatively in the study group(0.428±0.047 vs.0.342±0.045,P＜0.05)and the control group(0.401±0.051 vs.0.347±0.048,P＜0.05).While in the CST-damaged subgroup,no significant differences were found in FA value of the affected CST 1 month postoperatively compared with that preoperatively in both the experimental and control groups(bothP＞0.05).A significantly higher FA values 1 month postoperatively(0.428±0.047 vs.0.401±0.051,P=0.036)and good prognosis rate(9/12 vs.4/16,P=0.020)were observed in the CST-intact subgroup of the experimental group comparing with that of the control group,while there was no statistically significant difference in the 6-month mortality rate between the groups within the CST-intact subgroup(P=1.000).There were no statistically significant differences between the experimental group and the control group in both efficacy and safety indices within the CST-damaged subgroup(all P＞0.05).Conclusions Infarct core resection combining DC with CST protection demonstrates superior neurological functional improvement in comparison with anterior temporal lobectomy combining DC in treating MCI with malignant brain edema,particularly for patients with an intact CST before surgery(as indicated in patients'preoperative imaging results).This(infarct core resection combining DC with CST protection)approach does not increase the incidence of surgical complications.Prospective large sample controlled studies are required for further validation.