Neurological disorders such as post-stroke hemiplegia, spinal cord injury, and Parkinson disease represent a major global health burden. Brain-computer interface (BCI), which creates direct communication pathways between the nervous system and external devices, offers a promising strategy for functional restoration. The long-term efficacy of such BCI fundamentally depends on the performance of biomaterials at the neural interface. Ideal materials must concurrently satisfy biocompatibility, electrical conductivity, enduring chemical stability, and mechanical compatibility with brain tissue. This review systematically outlines the application of conductive polymers, inorganic nanomaterials, natural biomaterials, and composites in BCI, with a focus on how advanced designs, such as bionic and encapsulated electrodes, improve signal fidelity and surgical feasibility through structural innovation. It further summarizes key material-modification techniques and analyzes the complex foreign-body response orchestrated by microglia, astrocytes, and peripheral immune cells. Finally, it provides insights into future research directions and clinical translation of BCI-based neurorehabilitation, while highlighting critical challenges including long-term biosafety and the establishment of standardized evaluation frameworks, aiming to bridge the gap between laboratory innovation and effective clinical deployment.

[Purpose]To analyze the diagnostic yield of quantitative fecal immunochemical test(FIT)at different cut-offs in colorectal cancer(CRC)screening.[Methods]The sequential screening method was adapted in Jiashan CRC screening program for local residents aged 40～74 years old,which included a quantitative FIT and high-risk factor questionnaire for primary screening and subsequent colonoscopy for the diagnostic screening.Subjects who participated in quantitative FIT were included in this study between September,2021 and August,2023.The positive predictive values(PPVs)for colorectal neoplasms were calculated at the cut-offs of 100,120,140,160,180 and 200 ng/mL of FIT.The Cochran-Armitage trend test was performed to compare the trend of PPVs at different cut-offs.The effects of different starting age and FIT cut-offs on requirement of colonoscopy and advanced neoplasia detection were assessed.[Results]A total of 58 256 individuals completed the quantitative FIT,and 3 106 had fecal hemoglobin concentrations＞100 ng/mL,among whom 2 186 underwent colonoscopic examination with a compliance rate of 70.38％.The colonoscopy detected 588 cases of non-advanced adenomas and 355 cases of advanced neoplasms(AN),in-cluding 30 cases of CRC and 325 cases of advanced adenomas.Progressively increasing the cut-off showed a decrease in PPVs of non-advanced adenomas and an increase of AN.The ratio of the rate of reduced requirement of colonoscopy to the missed rate of the progressive lesions was the smallest when the screening start age was 45 years old and the positive FIT threshold was set at 100 ng/mL.[Conclusion]There were significant differences in the diagnostic yield at different cut-offs of FIT.Increasing the cut-offs of FIT will elevate PPVs for the advanced neoplasms.

Objective:To evaluate the protective effectiveness (VE) of the acellular pertussis vaccine (aPV) against pertussis in children aged 2 months to 6 years.Methods:A test-negative case-control study was conducted among children aged 2 months to 6 years who sought medical care for cough and underwent pertussis nucleic acid testing at sentinel surveillance hospitals in Zhejiang Province in 2024. Cases were defined as those with positive pertussis nucleic acid test results, while controls were test-negative individuals matched 1∶1 based on propensity scores using the caliper matching method. Conditional logistic regression models were used to calculate odds ratios ( ORs) and VEs. Results:Among the 658 participants, 31.76% (209 cases) tested positive for pertussis. After propensity score matching, 203 cases and 203 controls were included in the analysis. The VE of 1-2, 3, and 4 doses of aPV against pertussis was 52.46% (95% CI:-39.82%-83.84%), 65.22% (95% CI: 6.86%-87.02%), and 72.21% (95% CI: 34.33%-88.24%), respectively. For pertussis-related hospitalization, the VE of 1-3 and 4 doses was 80.95% (95% CI:31.38%-94.71%) and 86.79% (95% CI: 51.89%-96.37%). The VE for those who completed 4 doses of vaccination and had intervals of less than 2 years, 2 years, 3 years, and 4 years or more after vaccination were 91.15% (95% CI: 67.61%-97.58%), 84.70% (95% CI: 43.71%-95.84%),56.23% (95% CI:-47.58%-87.02%), and 49.92% (95% CI:-83.74%-86.35%), respectively. Conclusion:The VE of aPV against pertussis in children aged 2 months to 6 years increases with the number of doses administered, and it is more effective in preventing hospitalization due to pertussis. The VE declines rapidly over time after the last dose. It is recommended to follow the new pertussis immunization program for timely and full vaccination.

Objective To evaluate the diagnostic value of coiled-coil domain containing 83(CCDC83),carcinoembryonic antigen(CEA)and carbohydrate antigen(CA)199 detection in patients with colorectal cancer(CRC).Methods A total of 168 patients with colorectal diseases and 80 healthy physical examination subjects admitted to Hongqi Hospital Affiliated to Mudanjiang Medical University from September 2022 to January 2025 were selected as the study objects.Participants were classified into three groups based on pathological diagnosis and colonoscopy findings:colorectal cancer group(n=80),colorectal benign disease group(n=88),and healthy control group(n=80).Serum samples from all three groups were collected for detection of CCDC83,CEA,and CA199 expression levels.The diagnostic value was analyzed using receiver operating characteristic(ROC)curves.Results Colorectal cancer group exhibited significantly higher expression levels of CCDC83,CEA,and CA199 compared to colorectal benign disease group and healthy control group,with statistically significant differences(P＜0.05).ROC curve analysis demonstrated diagnostic value for CCDC83,CEA,and CA199,with CCDC83 showed superior specificity compared to CEA and CA199.Conclusion CCDC83,CEA and CA199 showed good diagnostic efficacy for colorectal cancer.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

BACKGROUND:Degenerative scoliosis is defined as a condition that occurs in adulthood with a coronal cobb angle of the spine>10° accompanied by sagittal deformity and rotational subluxation,which often produces symptoms of spinal cord and nerve compression,such as lumbar pain,lower limb pain,numbness,weakness,and neurogenic claudication.The finite element method is a mechanical analysis technique for computer modelling,which can be used for spinal mechanics research by building digital models that can realistically restore the human spine model and design modifications. OBJECTIVE:To review the application of finite element method in the etiology and treatment of degenerative scoliosis. METHODS:The literature databases CNKI,PubMed,and Web of Science were searched for articles on the application of finite element method in degenerative scoliosis published before October 2023.Search terms were"finite element analysis,biomechanics,stress analysis,degenerative scoliosis,adult spinal deformity"in Chinese and English.Fifty-four papers were finally included. RESULTS AND CONCLUSION:(1)The biomechanical findings from the degenerative scoliosis model constructed using the finite element method were identical to those from the in vivo experimental studies,which proves that the finite element method has a high practical value in degenerative scoliosis.(2)The study of the etiology and treatment of degenerative scoliosis by the finite element method is conducive to the prevention of the occurrence of the scoliosis,slowing down the progress of the scoliosis,the development of a more appropriate treatment plan,the reduction of complications,and the promotion of the patients'surgical operation.(3)The finite element method has gradually evolved from a single bony structure to the inclusion of soft tissues such as muscle ligaments,and the small sample content is increasingly unable to meet the research needs.(4)The finite element method has much room for exploration in degenerative scoliosis.

Drug resistance is one of the key factors affecting the effectiveness of cancer treatment methods, including chemotherapy, radiotherapy, and immunotherapy. Its occurrence is related to factors such as mRNA expression and methylation within cancer cells. If drug resistance in patients can be accurately identified early, doctors can devise more effective treatment plans, which is of great significance for improving patients' survival rates and quality of life. Cancer drug resistance prediction based on artificial intelligence (AI) technology has emerged as a current research hotspot, demonstrating promising application prospects in guiding clinical individualized and precise medication for cancer patients. This review aims to comprehensively summarize the research progress in utilizing AI algorithms to analyze multi-omics data including genomics, transcriptomics, epigenomics, proteomics, metabolomics, radiomics, and histopathology, for predicting cancer drug resistance. It provides a detailed exposition of the processes involved in data processing and model construction, examines the current challenges faced in this field and future development directions, with the aim of better advancing the progress of precision medicine.

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin regions, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at the single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences on TF binding. Notably, cFOOT-seq, combined with FootTrack analysis, enables de novo prediction of TF binding sites and tracking of TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.
Transcription Factors/genetics* ; Humans ; Chromatin/genetics* ; Animals ; Binding Sites ; Mice ; DNA Footprinting/methods*

OBJECTIVE: Chronic obstructive pulmonary disease (COPD) is a major health concern in northwest China; however, the impact of particulate matter (PM) exposure during sand-dust storms (SDS) remains poorly understood. Therefore, this study aimed to investigate the association between PM exposure on SDS days and COPD hospitalization risk in arid regions. METHODS: Data on daily COPD hospitalizations were collected from 323 hospitals from 2018 to 2022, along with the corresponding air pollutant and meteorological data for each city in Gansu Province. Employing a space-time-stratified case-crossover design and conditional Poisson regression, we analyzed 265,379 COPD hospitalizations. RESULTS: PM exposure during SDS days significantly increased COPD hospitalization risk [relative risk ( RR) for PM _2.5, lag 3:1.028, 95% confidence interval ( CI): 1.021-1.034], particularly among men and the elderly, and during the cold season. The burden of PM exposure on COPD hospitalization was substantially high in Northwest China, especially in the arid and semi-arid regions. CONCLUSION Our findings revealed a positive correlation between PM exposure during SDS episodes and elevated hospitalization rates for COPD in arid and semi-arid zones in China. This highlights the urgency of developing region-specific public health strategies to address adverse respiratory outcomes associated with SDS-related air quality deterioration.
Humans ; China/epidemiology* ; Pulmonary Disease, Chronic Obstructive/chemically induced* ; Particulate Matter/analysis* ; Hospitalization/statistics & numerical data* ; Male ; Female ; Middle Aged ; Aged ; Air Pollutants/analysis* ; Environmental Exposure/adverse effects* ; Spatio-Temporal Analysis ; Adult ; Sand ; Air Pollution

Background::B-cell maturation antigen (BCMA)-directed chimeric antigen receptor T (CAR-T) therapy yield remarkable responses in patients with relapsed/refractory multiple myeloma (R/RMM). Circulating tumor DNA (ctDNA) reportedly exhibits distinct advantages in addressing the challenges posed by tumor heterogeneity in the distribution and genetic variations in R/RMM.Methods::Herein, the ctDNA of 108 peripheral blood plasma samples from patients with R/RMM at the First Affiliated Hospital, School of Medicine, Zhejiang University was thoroughly investigated before administration of anti-BCMA CAR-T therapy to establish its predictive potential. Flow cytometry is used primarily to detect subgroups of T cells or CAR-T cells.Results::In this study, several tumor and T cell effector-mediated factors were considered to be related to treatment failure by an integrat analysis, including higher percentages of multiple myeloma (MM) cells in the bone marrow ( P = 0.0125), lower percentages of CAR-T cells in the peripheral blood at peak ( P = 0.0375), and higher percentages of CD8 + T cells ( P = 0.0340). Furthermore, there is a substantial correlation between high ctDNA level (>143 ng/mL) and shorter progression-free survival (PFS) ( P = 0.007). Multivariate Cox regression analysis showed that high levels of ctDNA (>143 ng/mL), MM-driven high-risk mutations (including IGLL5 [ P = 0.004], IRF4 [ P = 0.024], and CREBBP [ P = 0.041]), number of multisite mutations, and resistance-related mutation ( ERBB4, P = 0.040) were independent risk factors for PFS. Conclusion::Finally, a ctDNA-based risk model was built based on the above independent risk factors, which serves as an adjunct non-invasive measure of substantial tumor burden and a prognostic genetic feature that can assist in predicting the response to anti-BCMA CAR-T therapy.