Gorham-Stout disease(GSD) is a rare osteolytic disorder characterized by spontaneous and progressive osteolysis, along with abnormal angiogenesis and lymphangiogenesis, with no new bone formation. We present a case of a 15-year-old female admitted due to " recurrent right leg pain for 5 years, 11 months after undergoing right femoral fracture surgery". Through comprehensive integration of the patient's clinical phenotype, laboratory tests, imaging findings, pathological examinations, and molecular biological test results, GSD was considered highly likely. A multidisciplinary treatment approach was conducted, including a combination of zoledronic acid and sirolimus to inhibit osteolysis, along with rehabilitation training and orthopedic intervention, providing a personalized and comprehensive treatment strategy.

OBJECTIVE: To identify the underlying mechanism by which quercetin (Que) alleviates sepsis-related acute respiratory distress syndrome (ARDS). METHODS: In vivo, C57BL/6 mice were assigned to sham, cecal ligation and puncture (CLP), and CLP+Que (50 mg/kg) groups (n=15 per group) by using a random number table. The sepsisrelated ARDS mouse model was established using the CLP method. In vitro, the murine alveolar macrophages (MH-S) cells were classified into control, lipopolysaccharide (LPS), LPS+Que (10 μmol/L), and LPS+Que+acetylcysteine (NAC, 5 mmol/L) groups. The effect of Que on oxidative stress, inflammation, and apoptosis in mice lungs and MH-S cells was determined, and the mechanism with reactive oxygen species (ROS)/p38 mitogen-activated protein kinase (MAPK) pathway was also explored both in vivo and in vitro. RESULTS: Que alleviated lung injury in mice, as reflected by a reversal of pulmonary histopathologic changes as well as a reduction in lung wet/dry weight ratio and neutrophil infiltration (P<0.05 or P<0.01). Additionally, Que improved the survival rate and relieved gas exchange impairment in mice (P<0.01). Que treatment also remarkedly reduced malondialdehyde formation, superoxide dismutase and catalase depletion, and cell apoptosis both in vivo and in vitro (P<0.05 or P<0.01). Moreover, Que treatment diminished the release of inflammatory factors interleukin (IL)-1β, tumor necrosis factor-α, and IL-6 both in vivo and in vitro (P<0.05 or P<0.01). Mechanistic investigation clarifified that Que administration led to a decline in the phosphorylation of p38 MAPK in addition to the suppression of ROS expression (P<0.01). Furthermore, in LPS-induced MH-S cells, ROS inhibitor NAC further inhibited ROS/p38 MAPK pathway, as well as oxidative stress, inflammation, and cell apoptosis on the basis of Que treatment (P<0.05 or P<0.01). CONCLUSION Que was found to exert anti-oxidative, anti-inflammatory, and anti-apoptotic effects by suppressing the ROS/p38 MAPK pathway, thereby conferring protection for mice against sepsis-related ARDS.
Animals ; Sepsis/drug therapy* ; Quercetin/therapeutic use* ; Respiratory Distress Syndrome/enzymology* ; p38 Mitogen-Activated Protein Kinases/metabolism* ; Mice, Inbred C57BL ; Reactive Oxygen Species/metabolism* ; Apoptosis/drug effects* ; Male ; Oxidative Stress/drug effects* ; MAP Kinase Signaling System/drug effects* ; Lung/drug effects* ; Mice ; Lipopolysaccharides ; Macrophages, Alveolar/pathology* ; Inflammation/pathology* ; Protective Agents/therapeutic use*

Objective To investigate the efficacy of Yiqi Wenyang Sanjie Decoction in the treatment of patients with pulmonary ground glass nodules and yang-deficiency constitution,and to observe its effect on serum inflammatory factors and immune function.Methods From January 2020 to June 2024,a total of 106 patients with pulmonary ground glass nodules and yang-deficiency constitution admitted to the Department of Respiratory Medicine and Preventive Treatment of Disease Center of Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine were selected.The patients were randomly divided into control group and Chinese medicine group by random number table method,with 53 cases in each group.The patients in the control group were only given follow-up without any intervention,and the patients in the Chinese medicine group were treated with Yiqi Wenyang Sanjie Decoction for 3 months.Before and after treatment,the two groups were observed in the changes of traditional Chinese medicine(TCM)syndrome scores,maximum diameter of pulmonary ground glass nodules,serum levels of inflammatory factors of interleukin-6(IL-6),interleukin-17A(IL-17A),and tumor necrosis factor-α(TNF-α),and levels of immune function indicators of peripheral blood CD3+,CD4+cell percentage and CD4+/CD8+ratio.After treatment,the efficacy and safety of the two groups were evaluated.Results(1)During the trial,5 cases fell off from the control group and 3 cases fell off from the Chinese medicine group.A total of 98 cases were eventually included,and 48 cases were in the control group and 50 cases were in the Chinese medicine group.(2)After 3 months of treatment,the total effective rate of the Chinese medicine group was 46.00%(23/50),and that of the control group was 10.42%(5/48).The intergroup comparison(tested by chi-square test)showed that the efficacy of the Chinese medicine group was significantly superior to that of the control group(P＜0.01).(3)After treatment,the TCM syndrome score and the maximum diameter of pulmonary ground glass nodules in the Chinese medicine group were lowered(P＜0.05),while the TCM syndrome score and the maximum diameter showed no obvious changes compared with those at enrollment(P＞0.05);the intergroup comparison showed that the reduction of TCM syndrome score and the maximum diameter of pulmonary ground glass nodules in the Chinese medicine group was significantly superior to that in the control group(P＜0.05).(4)After treatment,the levels of serum inflammatory factors of IL-6,IL-17A and TNF-α in the Chinese medicine group were significantly lowered compared with those at enrollment(P＜0.05),while there was no significant change in serum IL-6,IL-17A and TNF-α levels of the control group(P＞0.05);the intergroup comparison showed that the decrease of serum IL-6,IL-17A and TNF-α levels in the Chinese medicine group was significantly superior to that in the control group(P＜0.05).(5)After treatment,the percentage of T lymphocyte subset CD4+and the ratio of CD4+/CD8+in the Chinese medicine group were significantly increased compared with those at enrollment(P＜0.05),while there was no significant change in the percentage of CD3+,CD4+cells and the ratio of CD4+/CD8+of the control group and in the percentage of CD3+cells of the Chinese medicine group(P＞0.05).The intergroup comparison showed that the increase of CD4+percentage and CD4+/CD8+ratio in the Chinese medicine group was significantly superior to that in the control group(P＜0.05).(6)There were no obvious adverse reactions occurring in the Chinese medicine group during the treatment,showing relatively high safety.Conclusion Yiqi Wenyang Sanjie Decoction exerts certain effect in the treatment of patients with pulmonary ground glass nodules and yang-deficiency constitution.It can effectively promote the reduction of nodules,relieve clinical symptoms,decrease serum levels of inflammatory factors,improve immune function,and has good safety.

Objective To propose a classification model based on a pseudo-bag strategy and feature adjustment for whole slide imaging in pathology.Methods A pseudo-bag generator was constructed to divide a parent bag into 3 pseudo-bags for increasing the number of training bags.Then,a pseudo-bag learning method based on Nystr?m-based algorithm for approximating self-attention and a selective feature fusion method were employed to process the pseudo-bags.Specifically,the pseudo-bag learning method based on Nystr?m-based algorithm for approximating self-attention reduced computational complexity through an improved multi-head self-attention mechanism while deeply extracting instance features to obtain pseudo-bag classification predictions,thereby enhancing pseudo-bag classification accuracy;and the selective feature fusion method refined pseudo-bag features by filtering and extracting relevant instances.Finally,the model adjusted bag features by extracting confounding factors to avoid interference from irrelevant information and further improve classification accuracy.Results The proposed model was evaluated on two datasets(CAMELYON-16 and TCGA-NSCLC)and compared with 10 other methods,and the results demonstrated that the proposed model achieved the best performance.The proposed method reached an accuracy of 0.943 on the CAMELYON-16 dataset and 0.906 on the TCGA-NSCLC dataset.Conclusion The proposed model can significantly improve the accuracy of whole-slide pathological image classification by effectively mitigating the overfitting and avoiding interference from irrelevant information.

A VLi-net based cell nucleus segmentation method integrating convolutional neural networks(CNN)and Vision Transformer(ViT)is proposed to address the limitation that the U-Net with CNN as its backbone is only proficient in capturing local features and has a restricted receptive field.Firstly,to mitigate challenges such as high cost of data annotation and insufficient annotated data,text annotations are introduced to enhance the network's understanding of image information.Secondly,to improve the segmentation performance of VLi-net,ViT and CNN are combined to fully extract global and local features,with multi-receptive field convolution features incorporating into the ViT structure for effectively mitigating the issues of limited local information interaction and single feature representation in ViT.Finally,an interactive fusion module(ViFusion)is used to efficiently fuse the multi-level features from the CNN and ViT branches.Experimental results show that VLi-net achieves a Dice coefficient of 80.85%and a mean intersection over union(MIoU)of 66.83%on the MoNuSeg dataset,obtains a Dice coefficient of 80.53%and a MIoU of 67.54%on the DSB-2018 dataset,and has a Dice coefficient of 86.87%and a MIoU of 77.44%on the TNBC dataset.These findings confirm that VLi-net outperforms other methods across multiple experimental metrics.

Objective To development and validate a predictive model for distinguishing between malignant pleural effusion(MPE)and benign pleural effusion(BPE).Methods A total of 428 patients diagnosed with pleural effusion(PE)and hospitalized at the First Hospital of Huai'an Affiliated to Nanjing Medical University from July 2020 to May 2022 were selected.The patients were divided into BPE group(211 cases)and MPE group(217 cases)according to diagnostic criteria.The basic information and clinical data of these patients were collected.Boruta method was used for univariate screening,followed by multivariate Logistic regression to construct a basic nomogram model.Bootstrap method was used for internal validation to evaluate the performance of the nomogram,including dis-crimination,accuracy,and clinical applicability.Results The model included 8 key variables:dyspnea,chest pain,general symp-toms,X-ray/CT with malignant tumor features,serum carcinoembryonic antigen,serum neuron-specific enolase,pleural lactate dehy-drogenase,and pleural carcinoembryonic antigen.Internal validation showed that the area under the receiver operating characteristic curve(AUCROC)of the model was 0.933(95%confidence interval:0.912-0.954),with good accuracy(P＞0.05).Decision curve a-nalysis(DCA)indicated that this predictive model for predicting MPE risk had a significant net benefit when the probability threshold exceeded 1%.Conclusion The constructed prediction model could effectively distinguish between MPE and BPE.

A VLi-net based cell nucleus segmentation method integrating convolutional neural networks(CNN)and Vision Transformer(ViT)is proposed to address the limitation that the U-Net with CNN as its backbone is only proficient in capturing local features and has a restricted receptive field.Firstly,to mitigate challenges such as high cost of data annotation and insufficient annotated data,text annotations are introduced to enhance the network's understanding of image information.Secondly,to improve the segmentation performance of VLi-net,ViT and CNN are combined to fully extract global and local features,with multi-receptive field convolution features incorporating into the ViT structure for effectively mitigating the issues of limited local information interaction and single feature representation in ViT.Finally,an interactive fusion module(ViFusion)is used to efficiently fuse the multi-level features from the CNN and ViT branches.Experimental results show that VLi-net achieves a Dice coefficient of 80.85%and a mean intersection over union(MIoU)of 66.83%on the MoNuSeg dataset,obtains a Dice coefficient of 80.53%and a MIoU of 67.54%on the DSB-2018 dataset,and has a Dice coefficient of 86.87%and a MIoU of 77.44%on the TNBC dataset.These findings confirm that VLi-net outperforms other methods across multiple experimental metrics.

Objective To propose a classification model based on a pseudo-bag strategy and feature adjustment for whole slide imaging in pathology.Methods A pseudo-bag generator was constructed to divide a parent bag into 3 pseudo-bags for increasing the number of training bags.Then,a pseudo-bag learning method based on Nystr?m-based algorithm for approximating self-attention and a selective feature fusion method were employed to process the pseudo-bags.Specifically,the pseudo-bag learning method based on Nystr?m-based algorithm for approximating self-attention reduced computational complexity through an improved multi-head self-attention mechanism while deeply extracting instance features to obtain pseudo-bag classification predictions,thereby enhancing pseudo-bag classification accuracy;and the selective feature fusion method refined pseudo-bag features by filtering and extracting relevant instances.Finally,the model adjusted bag features by extracting confounding factors to avoid interference from irrelevant information and further improve classification accuracy.Results The proposed model was evaluated on two datasets(CAMELYON-16 and TCGA-NSCLC)and compared with 10 other methods,and the results demonstrated that the proposed model achieved the best performance.The proposed method reached an accuracy of 0.943 on the CAMELYON-16 dataset and 0.906 on the TCGA-NSCLC dataset.Conclusion The proposed model can significantly improve the accuracy of whole-slide pathological image classification by effectively mitigating the overfitting and avoiding interference from irrelevant information.

Objective To development and validate a predictive model for distinguishing between malignant pleural effusion(MPE)and benign pleural effusion(BPE).Methods A total of 428 patients diagnosed with pleural effusion(PE)and hospitalized at the First Hospital of Huai'an Affiliated to Nanjing Medical University from July 2020 to May 2022 were selected.The patients were divided into BPE group(211 cases)and MPE group(217 cases)according to diagnostic criteria.The basic information and clinical data of these patients were collected.Boruta method was used for univariate screening,followed by multivariate Logistic regression to construct a basic nomogram model.Bootstrap method was used for internal validation to evaluate the performance of the nomogram,including dis-crimination,accuracy,and clinical applicability.Results The model included 8 key variables:dyspnea,chest pain,general symp-toms,X-ray/CT with malignant tumor features,serum carcinoembryonic antigen,serum neuron-specific enolase,pleural lactate dehy-drogenase,and pleural carcinoembryonic antigen.Internal validation showed that the area under the receiver operating characteristic curve(AUCROC)of the model was 0.933(95%confidence interval:0.912-0.954),with good accuracy(P＞0.05).Decision curve a-nalysis(DCA)indicated that this predictive model for predicting MPE risk had a significant net benefit when the probability threshold exceeded 1%.Conclusion The constructed prediction model could effectively distinguish between MPE and BPE.

Objective:To explore the impact of transcatheter edge-to-edge repair (TEER) devices on mitral valve regurgitant flow convergence post-procedure using computational fluid dynamics(CFD), and to seek solutions for accurately measuring effective regurgitant orifice area(EROA) after TEER.Methods:Multimodal fusion was conducted based on preoperative cardiac CT images and intraoperative three-dimensional transesophageal echocardiography(3DTEE) of 33 patients with mitral valve regurgitation undergoing TEER at Renmin Hospital of Wuhan University from January 2020 to August 2023. Using this data, CFD models of mitral valve regurgitation post-TEER, including with and without the TEER device, were constructed. The distance (D) from the midpoint of the mitral regurgitation orifice to the TEER device was measured. The proximal isovelocity surfice area(PISA) radius with and without the TEER device was measured, and the corresponding EROA1 and EROA2 based on this was calculated. The EROA correction factor CC=EROA2/EROA1 was calculated.Results:A total of 42 sets of CFD models with mild or greater residual mitral regurgitation, both with and without the TEER device, and 50 sets of PISA were obtained. Based on the relative position of PISA to the TEER device, four types of PISA were observed: Type 1: PISA away from the TEER device (D>R, 14 cases), with a CC of 0.93±0.07; Type 2: PISA adjacent to the TEER device (D