Accurate identification of the glomerular ultrastructure is critical for the diagnosis of chronic kidney diseases,but the high cost of acquiring high-quality annotated data limits the application of fully-supervised learning.Therefore,a multi-class semi-supervised semantic segmentation framework based on segment anything model(MC4S-SAM)is proposed.After improving the mask decoder of segment anything model to enable multi-class semantic segmentation without requiring prompt information,the improved model is used to generate and refine pseudo-labels through a self-training strategy,and multi-level consistency regularization constraints are incorporated to enhance the model's performance.Experimental results show that,in the task of segmenting the glomerular mesangial ultrastructure,MC4S-SAM outperformes the fully-supervised model by 11.72%in mean intersection over union(mIoU)and 11.45%in mean Dice similarity coefficient(mDSC)when the labeled data accountes for 1/16 of the total.When the labeled data proportion is 1/4,the mIoU and mDSC reach 68.91%and 78.73%,respectively,demonstrating its significant potential for aiding the diagnosis of chronic kidney diseases.

In view of the complex structure of diabetic retinopathy and the large differences in the scales of different lesions,a novel network which integrates deformable convolution and attention mechanism is proposed for automatic diabetic retinopathy multi-lesion segmentation.Specifically,deformable convolution Haar wavelet transform encoder takes place of the original convolutional downsampling encoder to adapt to the irregular shape changes of lesions and extract effective feature information;a dense feature perception and aggregation module is introduced at the bottleneck layer to extract multi-scale features by aggregating multiple receptive fields,thus enhancing deep semantic information;and finally,in order to fully integrate the decoder output and improve the recognition accuracy of edge information,a multi scale adaptive fusion module is used to weight the decoder output of each layer for obtaining the most accurate segmentation feature map.The validation of hard percolation,bleeding point,and soft percolation segmentations on the DDR-RLS dataset reveals that the proposed network shows increases of 0.026 2,0.051 8 and 0.046 5 in IoU coefficient,0.027 1,0.058 1 and 0.050 4 in Dice coefficient,and 0.0423,0.0691 and 0.0734 in AUPR value,as compared with the original Unet.

BACKGROUND:As a temporary matrix for new bone growth,the porous scaffold plays a key role in the process of bone repair.The structural design of porous scaffolds is a research priority in the process of bone repair.OBJECTIVE:To summarize traditional bone scaffolds(regular,uniform scaffolds)and bionic scaffolds(irregular,inhomogeneous scaffolds)in the field of bone tissue engineering research.METHODS:A computerized search was performed in the databases of CNKI,VIP,WanFang,Web of Science,Science Direct,PubMed,and EI.Literature published from January 2008 to March 2024 was selected.The search terms in Chinese included"bone tissue engineering,bionic scaffolds,bone trabeculae,traditional scaffolds,bone repair,triple-period minimal surfaces."The search terms in English were"bone tissue engineering,bionic scaffolds,bone trabeculae,traditional scaffolds,bone repair,TPMS."Finally,81 articles were included for review.RESULTS AND CONCLUSION:The structural design of bone scaffolds is the key to achieve bone repair and bone regeneration,and scaffold technology in bone tissue engineering has made remarkable progress.Traditional regular porous scaffolds are widely used due to their simple manufacturing process and good mechanical properties.However,these scaffolds often lack biological activity and are difficult to mimic the complex microenvironment of natural bone tissue,limiting their ability to promote cell proliferation and bone regeneration.On the contrary,bionic scaffolds provide a more suitable physiological microenvironment by mimicking the structural features of natural bone tissues,which promotes the proliferation and differentiation of osteoblasts,as well as the formation of new bone,and provides a new way of thinking for the effective treatment of bone defects.Despite the great potential of bionic scaffolds in theory,they still face many challenges in practical applications.Factors such as the scaffold's biocompatibility,bioactivity,and its long-term stability still need to be further verified through clinical trials.

ObjectiveTo investigate the triglyceride-glucose (TyG) index and the level of serum homocysteine (Hcy) in association with the incidence of stroke in type 2 diabetes mellitus (T2DM) patients. MethodsBased on the chronic disease risk factor surveillance cohort in Pudong New Area, Shanghai, excluding those with stroke in baseline survey, T2DM patients who joined the cohort from January 2016 to October 2020 were selected as the research subjects. During the follow-up period, a total of 318 new-onset ischemic stroke patients were selected as the case group, and a total of 318 individuals matched by gender without stroke were selected as the control group. The Cox proportional hazards regression model was used to adjust for confounding factors and explore the serum TyG index and the Hcy biochemical indicator in association with the risk of stroke. ResultsThe Cox proportional hazards regression results showed that after adjusting for confounding factors, the risk of stroke in T2DM patients with 10 μmol·L⁻¹-¹ and Hcy>15 μmol·L⁻¹ was 1.532 times (95%CI: 1.017‒2.309 times, P=0.041) and 1.738 times (95%CI: 1.119‒2.699 times, P=0.014) higher respectively, compared to T2DM patients with Hcy≤10 μmol·L⁻¹. T2DM patients with TyG>9.074 had 1.396 times higher risk of stroke (95%CI: 1.091‒1.787, P=0.008) compared to those with TyG≤9.074. The study found that urea and α1-antitrypsin (α1-AT) were independent risk factors for the incidence of stroke in T2DM patients. For every unit increase in urea andα1-AT, the risk of stroke in T2DM patients increased by 233.6% (HR=3.336, 95%CI: 1.588‒7.009, P=0.001) and 11.3% (HR=1.113, 95%CI: 1.028‒1.205, P=0.008), respectively. Cumulative risk curves showed that Hcy>10 μmol·L⁻¹ and TyG>9.074 accelerated the time to stroke occurrence in T2DM patients. ConclusionElevated levels of Hcy>10 μmol·L⁻¹ and a higher TyG index are risk factors for stroke in T2DM patients. Effective interventions for Hcy and TyG should be conducted for diabetic patients to reduce the incidence of stroke.

In view of the complex structure of diabetic retinopathy and the large differences in the scales of different lesions,a novel network which integrates deformable convolution and attention mechanism is proposed for automatic diabetic retinopathy multi-lesion segmentation.Specifically,deformable convolution Haar wavelet transform encoder takes place of the original convolutional downsampling encoder to adapt to the irregular shape changes of lesions and extract effective feature information;a dense feature perception and aggregation module is introduced at the bottleneck layer to extract multi-scale features by aggregating multiple receptive fields,thus enhancing deep semantic information;and finally,in order to fully integrate the decoder output and improve the recognition accuracy of edge information,a multi scale adaptive fusion module is used to weight the decoder output of each layer for obtaining the most accurate segmentation feature map.The validation of hard percolation,bleeding point,and soft percolation segmentations on the DDR-RLS dataset reveals that the proposed network shows increases of 0.026 2,0.051 8 and 0.046 5 in IoU coefficient,0.027 1,0.058 1 and 0.050 4 in Dice coefficient,and 0.0423,0.0691 and 0.0734 in AUPR value,as compared with the original Unet.

Accurate identification of the glomerular ultrastructure is critical for the diagnosis of chronic kidney diseases,but the high cost of acquiring high-quality annotated data limits the application of fully-supervised learning.Therefore,a multi-class semi-supervised semantic segmentation framework based on segment anything model(MC4S-SAM)is proposed.After improving the mask decoder of segment anything model to enable multi-class semantic segmentation without requiring prompt information,the improved model is used to generate and refine pseudo-labels through a self-training strategy,and multi-level consistency regularization constraints are incorporated to enhance the model's performance.Experimental results show that,in the task of segmenting the glomerular mesangial ultrastructure,MC4S-SAM outperformes the fully-supervised model by 11.72%in mean intersection over union(mIoU)and 11.45%in mean Dice similarity coefficient(mDSC)when the labeled data accountes for 1/16 of the total.When the labeled data proportion is 1/4,the mIoU and mDSC reach 68.91%and 78.73%,respectively,demonstrating its significant potential for aiding the diagnosis of chronic kidney diseases.

BACKGROUND:As a temporary matrix for new bone growth,the porous scaffold plays a key role in the process of bone repair.The structural design of porous scaffolds is a research priority in the process of bone repair.OBJECTIVE:To summarize traditional bone scaffolds(regular,uniform scaffolds)and bionic scaffolds(irregular,inhomogeneous scaffolds)in the field of bone tissue engineering research.METHODS:A computerized search was performed in the databases of CNKI,VIP,WanFang,Web of Science,Science Direct,PubMed,and EI.Literature published from January 2008 to March 2024 was selected.The search terms in Chinese included"bone tissue engineering,bionic scaffolds,bone trabeculae,traditional scaffolds,bone repair,triple-period minimal surfaces."The search terms in English were"bone tissue engineering,bionic scaffolds,bone trabeculae,traditional scaffolds,bone repair,TPMS."Finally,81 articles were included for review.RESULTS AND CONCLUSION:The structural design of bone scaffolds is the key to achieve bone repair and bone regeneration,and scaffold technology in bone tissue engineering has made remarkable progress.Traditional regular porous scaffolds are widely used due to their simple manufacturing process and good mechanical properties.However,these scaffolds often lack biological activity and are difficult to mimic the complex microenvironment of natural bone tissue,limiting their ability to promote cell proliferation and bone regeneration.On the contrary,bionic scaffolds provide a more suitable physiological microenvironment by mimicking the structural features of natural bone tissues,which promotes the proliferation and differentiation of osteoblasts,as well as the formation of new bone,and provides a new way of thinking for the effective treatment of bone defects.Despite the great potential of bionic scaffolds in theory,they still face many challenges in practical applications.Factors such as the scaffold's biocompatibility,bioactivity,and its long-term stability still need to be further verified through clinical trials.

Background The serious air pollution of highway toll booths poses a high occupational exposure risk to toll collectors. It is urgent to develop purification methods suitable for airborne particles and microbial pathogens in highway toll booths. Objective To verify the purification effect of low temperature plasma air purifiers on airborne particles and microbes in highway toll booths. Methods Based on controlled-intervention design, we selected three toll booths in an expressway toll station as on-site experimental locations for 6 d (no-intervention period: the low-temperature plasma purifier was turned off in the first three days; intervention period: the purifier was turned on from 9:00 to 17:00 in the following three days). The indoor and outdoor PM_2.5 and PM₁₀ concentrations were continuously monitored during the study. At 9:00, 12:00, and 17:00 of every day during the experiment, indoor and outdoor air samples were collected to analyze the concentration of airborne culturable colonies with a plankton sampler. Airborne particle samples were collected in the outermost exit continuously from 9:00 to 17:00 every day during the experiment using a medium flow particulate sampler, and the species and relative abundance of fungi and bacteria contained in the samples were analyzed by gene sequencing. Independent-sample t test was used to compare the concentration of indoor PM_2.5, PM₁₀, and culturable colonies between the intervention period and the non-intervention period. α diversity analysis, β diversity analysis, and t test were used to compare the diversity and relative abundance of specific species of bacteria and fungi, as well as typical pathogenic bacteria and fungi in the samples between the non-intervention period and the intervention period to reflect the purification effect of low temperature plasma air purifier on airborne PM_2.5, PM₁₀, and microorganisms. Results During the intervention period, the mean indoor concentrations of PM_2.5, PM₁₀, and culturable colonies were lower than those of the no-intervention period (P<0.01 or P<0.001). The ratios of indoor to outdoor concentration (I/O) of PM_2.5 and PM₁₀ during the intervention period were significantly lower than those of the no-intervention period (P<0.001), except the I/O of culturable colonies. Compared with the average concentration at 9:00, the average cleaning rates at 12:00 and 17:00 for PM_2.5 were 49.0% and 46.1%, for PM₁₀ were 49.7% and 45.4%, for airborne culturable colonies were 50.8% and 49.9%, respectively. The β diversity analysis showed that there were significant differences in composition at the level of species of bacteria, and at the levels of genus and species of fungi between the intervention and the no-intervention periods. The relative abundances of 10 species of bacteria such as Lactobacillus and 7 species of fungi such as Torula in the intervention period were significantly lower than those in the non-intervention period, but the relative abundances of fungi such as unclassified_f_cladosporiaceae, trichomerium, and cercospora were higher (P<0.05). For typical pathogenic bacteria, the relative abundances of Lactobacillus and Clostridium_sensu_stricto_1 during the intervention period were 73.5% and 86.9% lower than those in the no-intervention period, and the relative abundance of Talaromyces was 53.5% lower (P<0.05). Conclusion Low temperature plasma air purifier has a good purification effect on indoor PM_2.5, PM₁₀, and culturable colonies in highway toll booths, and likely a limited effect on some fungi.

Objective To develop a multi-modal deep learning method for automatic classification of immune-mediated glomerular diseases based on images of optical microscopy(OM),immunofluorescence microscopy(IM),and transmission electron microscopy(TEM).Methods We retrospectively collected the pathological images from 273 patients and constructed a multi-modal multi-instance model for classification of 3 immune-mediated glomerular diseases,namely immunoglobulin A nephropathy(IgAN),membranous nephropathy(MN),and lupus nephritis(LN).This model adopts an instance-level multi-instance learning(I-MIL)method to select the TEM images for multi-modal feature fusion with the OM images and IM images of the same patient.By comparing this model with unimodal and bimodal models,we explored different combinations of the 3 modalities and the optimal methods for modal feature fusion.Results The multi-modal multi-instance model combining OM,IM,and TEM images had a disease classification accuracy of(88.34±2.12)%,superior to that of the optimal unimodal model[(87.08±4.25)%]and that of the optimal bimodal model[(87.92±3.06)%].Conclusion This multi-modal multi-instance model based on OM,IM,and TEM images can achieve automatic classification of immune-mediated glomerular diseases with a good classification accuracy.

Objective To develop a multi-modal deep learning method for automatic classification of immune-mediated glomerular diseases based on images of optical microscopy(OM),immunofluorescence microscopy(IM),and transmission electron microscopy(TEM).Methods We retrospectively collected the pathological images from 273 patients and constructed a multi-modal multi-instance model for classification of 3 immune-mediated glomerular diseases,namely immunoglobulin A nephropathy(IgAN),membranous nephropathy(MN),and lupus nephritis(LN).This model adopts an instance-level multi-instance learning(I-MIL)method to select the TEM images for multi-modal feature fusion with the OM images and IM images of the same patient.By comparing this model with unimodal and bimodal models,we explored different combinations of the 3 modalities and the optimal methods for modal feature fusion.Results The multi-modal multi-instance model combining OM,IM,and TEM images had a disease classification accuracy of(88.34±2.12)%,superior to that of the optimal unimodal model[(87.08±4.25)%]and that of the optimal bimodal model[(87.92±3.06)%].Conclusion This multi-modal multi-instance model based on OM,IM,and TEM images can achieve automatic classification of immune-mediated glomerular diseases with a good classification accuracy.