Osteoarthritis (OA) causes chronic pain that significantly impairs quality of life, with current treatments often proving insufficient and accompanied by adverse effects. Recent research has identified the dorsal root ganglion (DRG) and its resident macrophages as crucial mediators of chronic OA pain through neuroinflammation driven by macrophage polarization. We present a novel injectable thermo-sensitive hydrogel system, KAF@PLEL, designed to deliver an anti-inflammatory peptide (KAF) specifically to the DRG. This biodegradable hydrogel enables sustained KAF release, promoting the reprogramming of DRG macrophages from pro-inflammatory to anti-inflammatory phenotypes. Through comprehensive in vitro and in vivo studies, we evaluated the hydrogel's biocompatibility, effects on macrophage polarization, and therapeutic efficacy in chronic OA pain management. The system demonstrated significant capabilities in preserving macrophage mitochondrial function, suppressing neuroinflammation, alleviating chronic OA pain, reducing cartilage degradation, and improving motor function in OA rat models. The sustained-release properties of KAF@PLEL enabled prolonged therapeutic effects while minimizing systemic exposure and side effects. These findings suggest that KAF@PLEL represents a promising therapeutic approach for improving outcomes in OA patients through targeted, sustained treatment.

Postoperative infection of internal fixation of closed fractures the lower limbs in adults represents a devastating complication, characterized by diagnostic challenges, prolonged treatment duration and high disability rates. Current management of these infections faces multiple challenges, such as difficulties in early accurate diagnosis, and various controversies about the treatment plan, leading to poor overall diagnosis and treatment results. To address these issues, based on evidence-based medicine and principles with emphasis on scientific rigor, clinical applicability and innovation, the Trauma Branch of the Chinese Medical Association, Orthopedic Branch of the Chinese Medical Doctor Association, Orthopedics Branch of the Chinese Medical Association, and Trauma Orthopedics and Polytrauma Group of the Resuscitation and Emergency Committee of the Chinese Medical Doctor Association have collaboratively organized a panel of relevant experts to develop the Guideline for diagnosis and treatment of infection after internal fixation of closed lower limb fractures in adults ( version 2025). The guideline proposed 10 recommendations, aiming to provide a foundation for standardized diagnosis and treatment of postoperative infection in adults with closed lower limb fractures.

ObjectiveTo investigate a suspected outbreak of healthcare-associated infection with carbapenem-resistant Klebsiella pneumoniae (CRKP) in a geriatric emergency ward, and to provide references for the prevention and control of multidrug-resistant bacteria in a hospital in Shanghai. MethodsOn-site epidemiological investigation, combined with environmental hygiene monitoring and pulsed field gel electrophoresis (PFGE) molecular typing method, were adopted to investigate a suspected outbreak of CRKP infection in the geriatric emergency ward of a hospital from October to November 2022, aiming at finding out factors caused the outbreak before taking corresponding control measures. ResultsA total of 3 cases of healthcare-associated CRKP infection were identified, of which 2 cases were homologous to a previous case of community-associated CRKP infection. What’s more, the 2 cases lived in the same ward with the latter and with adjacent beds, but the third case was non-homologous to the community-associated infection case. A total of 46 samples were collected from the environmental surfaces and the hands of healthcare workers, of which 7 samples tested positive for CRKP and were identical to the strains from the 2 healthcare-associated infection cases and the 1 community-associated infection case, originating from the bedrails, bedside tables, surface of non-invasive ventilator, bed curtains and panels of monitoring equipment, with a detection rate of 15.22%. But none of the 11 samples from the hands of healthcare workers tested positive for CRKP. The outbreak was effectively controlled after taking specific prevention and control measures such as strengthening personnel management, intensifying environmental cleaning and disinfection and strictly enforcing hand hygiene among healthcare workers. Subsequently, no similar new cases were reported during the 14-day follow-up period. ConclusionIncomplete environmental cleaning and disinfection, as well as inadequate enforcement of hand hygiene among heatheare workers may have contributed to the suspected outbreak of CRKP in the geriatric emergency ward. Early warning and timely investigation of suspected outbreaks of multidrug-resistant bacteria are crucial for preventing and controlling such outbreaks in hospitals.

A novel approach named hierarchical vascular morphology awareness and noisy label refine for hepatic vessel segmentation(HN-Seg)is proposed to achieve precise vessel segmentation while reducing dependency on high-quality labels.HN-Seg comprises of(1)hierarchical vascular morphology aware network which employs a multi-scale local morphology attention mechanism and a global morphology preservation loss function to ensure the integrity of overall vascular morphology,and(2)self-distillation noisy label refine module which leverages the uncertainty in model outputs to optimize noisy labels through uncertainty optimization and consistency regularization,thereby maximizing the knowledge extracted from images during training and refining noisy labels.Experimental results on the hepatic vessel dataset demonstrate that HN-Seg achieves superior segmentation performance,outperforming 6 methods(UNet,UNet++,UNETR,SwinUNetR,FRUNet,and MTCL).HN-Seg attains DSC and clDice scores of 0.727 and 0.773,showing improvements of 9.6%and 21.5%over the baseline method UNETR.

Objective:To evaluate the feasibility of using the domestic ArcherQA system for fast and simplified independent verification of CyberKnife (CK) stereotactic radiotherapy (SRT) plans.Methods:SRT plans of 57 patients treated with CK at Tianjin Medical University Cancer Institute and Hospital from August 2021 to August 2022 were retrospectively analyzed, including 15 intracranial, 30 pulmonary, and 12 abdominal tumors cases. Point-dose and planar-dose verifications were performed using an ionization chamber and radiochromic films embedded in a homogeneous phantom, and the results were compared with those calculated by the treatment planning system (TPS). The localization CT images and corresponding SRT plans were imported into the ArcherQA system for independent dose verification and analysis. The correlation between ArcherQA results and phantom measurements was analyzed, with comparisons of target mean dose differences and γ pass rates.Results:Phantom measurement results showed, the measured point-dose differences for intracranial, lung, and abdominal plans were -0.94% ± 3.22%, 1.92% ± 2.05%, and 2.12% ± 0.77%, respectively. The mean dose differences in target dose calculation between ArcherQA and TPS: intracranial in the gross tumor volume (GTV) regions were 0.34% ± 2.21%, lung tumor GTV were -2.47% ± 2.46%, and abdominal tumor GTV were 0.80% ± 2.61%, respectively. Among them, the abdominal GTV region showed the highest correlation between ArcherQA and measured results ( r=0.78). The average two-dimensional γ pass rates (2 mm/2%, threshold=10%) measured using phantom films were 95.92% ± 2.35% for intracranial, 95.70% ± 2.74% for lung, and 96.74% ± 3.41% for abdominal tumors plans, respectively. The three-dimensional ArcherQA results showed comparable γ pass rates (1 mm/2%, threshold=10%) for lung and abdominal GTV and PTV regions, with similar medians and data dispersion to film measurements. Conclusions:The ArcherQA system enables rapid and efficient independent dose verification of CK SRT plans without the need for additional hardware. The verification results show good correlation with phantom measurements, supporting its potential as an auxiliary quality assurance tool in clinical CK SRT implementation.

Skull segmentation in magnetic resonance image(MRI)provides realistic skull models for MEG and EEG positive problems.An MRI skull segmentation method based on bi-stream pyramid decoder and deep supervision(BiNETR)is proposed to solve the problem of difficult segmentation due to the blurred and complex structure of MRI skull imaging.The method uses a bi-stream pyramid decoder as the main decoder in the network structure of encoding-decoding,including serial dual decoders for edge information oriented and precise feature merging.Specifically,edge information oriented pyramid decoder effectively enhances the edge information based on feature sharpening to improve the edge segmentation accuracy,and the precise feature merging pyramid decoder further refines and reuses the edge-enhanced features to promote the fusion of deep and shallow features.In addition,deep supervised computation of intermediate feature loss is introduced to implant the gradient into the deep network for enhancing network training.The segmentation algorithm is validated on the skull dataset,achieving a Dice similarity coefficient of 0.880±0.039 and an average symmetric surface distance of(0.931±0.286)mm,outperforming other state-of-the-art methods.The experimental results demonstrate the effectiveness and accuracy of the proposed algorithm in MRI skull segmentation.

A self-supervised super-resolution reconstruction method based on scale adaptive and coordinate encoding is proposed to realize super-resolution reconstruction of anisotropic brain magnetic resonance images with different slice thicknesses even in the absence of paired isotropic brain magnetic resonance images.Firstly,an image encoding module that integrates super-resolution scale information is used to learn the specific features of images with different slice thicknesses.Subsequently,a coordinate encoding module is employed to facilitate the deep fusion of coordinate information and image features.Finally,an overall loss function comprising reconstruction loss and brain tissue edge perception loss is adopted to optimize the recovery of edge high-frequency information,while global residual learning is introduced to enhance network training.Experimental results on the HCP-1200 and OASIS-1 datasets demonstrate that the proposed method outperforms other self-supervised super-resolution reconstruction methods.

A self-supervised super-resolution reconstruction method based on scale adaptive and coordinate encoding is proposed to realize super-resolution reconstruction of anisotropic brain magnetic resonance images with different slice thicknesses even in the absence of paired isotropic brain magnetic resonance images.Firstly,an image encoding module that integrates super-resolution scale information is used to learn the specific features of images with different slice thicknesses.Subsequently,a coordinate encoding module is employed to facilitate the deep fusion of coordinate information and image features.Finally,an overall loss function comprising reconstruction loss and brain tissue edge perception loss is adopted to optimize the recovery of edge high-frequency information,while global residual learning is introduced to enhance network training.Experimental results on the HCP-1200 and OASIS-1 datasets demonstrate that the proposed method outperforms other self-supervised super-resolution reconstruction methods.

Skull segmentation in magnetic resonance image(MRI)provides realistic skull models for MEG and EEG positive problems.An MRI skull segmentation method based on bi-stream pyramid decoder and deep supervision(BiNETR)is proposed to solve the problem of difficult segmentation due to the blurred and complex structure of MRI skull imaging.The method uses a bi-stream pyramid decoder as the main decoder in the network structure of encoding-decoding,including serial dual decoders for edge information oriented and precise feature merging.Specifically,edge information oriented pyramid decoder effectively enhances the edge information based on feature sharpening to improve the edge segmentation accuracy,and the precise feature merging pyramid decoder further refines and reuses the edge-enhanced features to promote the fusion of deep and shallow features.In addition,deep supervised computation of intermediate feature loss is introduced to implant the gradient into the deep network for enhancing network training.The segmentation algorithm is validated on the skull dataset,achieving a Dice similarity coefficient of 0.880±0.039 and an average symmetric surface distance of(0.931±0.286)mm,outperforming other state-of-the-art methods.The experimental results demonstrate the effectiveness and accuracy of the proposed algorithm in MRI skull segmentation.

A novel approach named hierarchical vascular morphology awareness and noisy label refine for hepatic vessel segmentation(HN-Seg)is proposed to achieve precise vessel segmentation while reducing dependency on high-quality labels.HN-Seg comprises of(1)hierarchical vascular morphology aware network which employs a multi-scale local morphology attention mechanism and a global morphology preservation loss function to ensure the integrity of overall vascular morphology,and(2)self-distillation noisy label refine module which leverages the uncertainty in model outputs to optimize noisy labels through uncertainty optimization and consistency regularization,thereby maximizing the knowledge extracted from images during training and refining noisy labels.Experimental results on the hepatic vessel dataset demonstrate that HN-Seg achieves superior segmentation performance,outperforming 6 methods(UNet,UNet++,UNETR,SwinUNetR,FRUNet,and MTCL).HN-Seg attains DSC and clDice scores of 0.727 and 0.773,showing improvements of 9.6%and 21.5%over the baseline method UNETR.