OBJECTIVE To evaluate the efficacy and safety of immune checkpoint inhibitors （ICIs） combined with neoadjuvant chemotherapy in the treatment of early triple-negative breast cancer （TNBC）. METHODS Randomized controlled trials （RCTs） comparing ICIs combined with neoadjuvant chemotherapy （experimental group） versus neoadjuvant chemotherapy alone （control group） were retrieved from PubMed， Cochrane Library， Embase， Web of Science， CNKI， Wanfang Data， and VIP databases， as well as relevant studies published at oncology academic conferences. The search period was from database inception to June 30， 2025. After literature screening， data extraction， and quality assessment， a meta-analysis was performed by using RevMan 5.4 software. RESULTS A total of 6 RCTs involving 3 786 patients were finally included. The meta-analysis results showed that the experimental group had superior event-free survival [HR＝0.73， 95%CI （0.62， 0.85）， P＜0.000 1]， overall survival [HR＝0.69， 95%CI （0.57， 0.84）， P＝0.000 3]， and pathological complete response （pCR） [OR＝1.57， 95%CI （1.37， 1.80）， P＜0.000 01] compared to the control group. The incidence of ≥grade 3 adverse event （AE）， severe AE （SAE）， and ≥ grade 3 immune-related adverse event （irAE） in the experimental group was significantly higher than that in the control group. There was no statistically significant difference between the two groups in the incidence of any AE or any irAE （P＞0.05）. Subgroup analysis revealed that， regardless of programmed cell death ligand 1 expression status （negative or positive），the pCR in the experimental group was significantly higher than that in the control group （P＜0.05）. Additionally， the pCR of the patients with positive lymph nodes in the experimental group was significantly higher to that in the ontrol group （P＜0.05）. There was no statistically significant difference in pCR between the two groups with negative lymph nodes （P＝0.09）. CONCLUSIONS ICIs combined with neoadjuvant chemotherapy can significantly improve event-free survival and overall survival in patients with TNBC， providing patients with long-term survival benefits. However， the risk of ≥ grade 3 AE， SAE and ≥ grade 3 irAE has increased.

Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer.Computational methods have been widely explored and have become increasingly accurate in recent years.However,the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients.We present a novel disentangled synthesis transfer network(DiSyn)for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients.DiSyn uses a domain separation network(DSN)to disentangle drug response related features,employs data synthesis technology to increase the sample size and iteratively trains for better feature disentanglement.DiSyn is pretrained on large-scale unlabeled cancer samples and validated by three datasets,The Cancer Genome Atlas(TCGA),Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And moLecular Analysis 2(I-SPY2)and Novartis Institutes for Biomedical Research Patient-Derived Xenograft Encyclopedia(NIBR PDXE),achieving competitive performance with the state-of-the-art methods on cancer patients and mice.Furthermore,the application of DiSyn to thousands of breast cancer patients show the heterogeneity in drug responses and demonstrate its potential value in biomarker discovery and drug combination prediction.

Objective:To investigate the role and possible mechanism of edaravone dexborneol in the prevention and control of patho-logical injury of brain tissue around hematoma after intracerebral hemorrhage(ICH).Methods:A total of 176 Sprague-Dawley rats were randomly divided into sham-operation group,ICH group,edaravone group,and edaravone dexborneol group,with 44 rats in each group.All rats except those in the sham-operation group were used to establish a rat model of acute ICH,and each group was further di-vided into 1-day,3-day,7-day,and 14-day subgroups according to the time of evaluation,with 11 rats in each subgroup.At 2 hours after modeling,drugs were administered via intraperitoneal injection,i.e.,6 ml/kg normal saline for the sham-operation group and the ICH group,6 mg/kg edaravone for the edaravone group,and 7.5 mg/kg edaravone dexborneol for the edaravone dexborneol group,fol-lowed by subsequent administration every 12 hours until day 14.The modified Neurological Severity Score(mNSS)was used to assess neurological function;the wet-dry weight method was used to measure the water content of brain tissue;the biochemical methods were used to measure nitric oxide(NO)and total antioxidant capacity(T-AOC)in brain tissue around the hematoma;immunohistochemistry was used to measure the content of CD16 and CD206 in brain tissue around the hematoma.Results:The mNSS score on day 3(peak of the disease)was 0.6±0.5 for the sham-operation group,14.0±1.6 for the ICH group,9.8±0.8 for the edaravone group,and 10.4±1.1 for the edaravone dexborneol group,with a water content of 63.2±2.14,85.61±1.43,81.48±1.9,and 76.77±1.44,respectively,and the content of NO was 2.45±0.46,9.98±0.54,8.77±0.36,and 7.92±0.43,respectively;the content of T-AOC was 0.67±0.02,0.74±0.03,0.78±0.02,and 0.84±0.03,respectively;the content of CD16 was 143.8±15.82,3 673.81±166.33,2 970.74±132.75,and 2 521.69±140.74,respectively;the content of CD206 was 548.46±93.68,726±97.81,915.28±100.33,and 1 119.51±160.52,respectively.There were significant differences in these indica-tors between the edaravone dexborneol group and the other three groups(all P<0.05).Conclusion:Edaravone dexborneol can alleviate neurological deficit caused by ICH and exert a neuroprotective effect by reducing brain tissue edema around the hematoma,alleviating oxidative stress,and regulating the polarization of microglial cells.

Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer. Computational methods have been widely explored and have become increasingly accurate in recent years. However, the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients. We present a novel disentangled synthesis transfer network (DiSyn) for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients. DiSyn uses a domain separation network (DSN) to disentangle drug response related features, employs data synthesis technology to increase the sample size and iteratively trains for better feature disentanglement. DiSyn is pretrained on large-scale unlabeled cancer samples and validated by three datasets, The Cancer Genome Atlas (TCGA), Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And moLecular Analysis 2 (I-SPY2) and Novartis Institutes for Biomedical Research Patient-Derived Xenograft Encyclopedia (NIBR PDXE), achieving competitive performance with the state-of-the-art methods on cancer patients and mice. Furthermore, the application of DiSyn to thousands of breast cancer patients show the heterogeneity in drug responses and demonstrate its potential value in biomarker discovery and drug combination prediction.

Parkinson disease(PD)is one of the most prevalent neurodegenerative disorders,typically affecting the elderly.The pathogenesis of PD is intricate and involves multiple factors,with mitochondrial dysfunction recognized as a significant mechanism contributing to its development.MicroRNAs(miRNAs)play a crucial role in the onset and pro-gression of PD.This review focuses on recent research concerning miRNAs associated with PD,particularly their impact on mitochondrial dysfunction.The insights gathered here aim to support the early diagnosis and targeted treatment of PD.

Cognitive decline is one of the most common adverse symptoms following acute high-altitude exposure(AHAE).MRI can reveal the structural and functional changes in brain caused by AHAE,thereby deeply understanding the specific mechanism of its cognitive decline,which is of great significance for early prevention and intervention.Research progresses on MRI assessment of cognitive function after AHAE and related intervention methods were reviewed in this article.

Objective:To analyze the clinical characteristics of anti-melanoma differentiation associated gene 5 (MDA5) antibody-positive juvenile dermatomyositis (JDM) patients.Methods:A retrospective case-control study was conducted. The positive group included 18 children with anti-MDA5 antibody-positive JDM who were admitted to the Department of Rheumatology and Immunology at Capital Center for Children′s Health with Capital Medical University between January 2016 and January 2023. Another 36 children with anti-MDA5 antibody-negative JDM hospitalized during the same period were enrolled as the negative group. Based on the extent of pulmonary involvement and pulmonary CT scores, the MDA5-positive group was further divided into severe pulmonary involvement and non-severe pulmonary involvement subgroups. Chi-square test and Kruskal-Wallis test were used to compare clinical features, laboratory test results between groups.Results:Among the 18 patients in the MDA5-positive group, 7 were male and 11 were female, with an age of onset of 5.0 (2.6, 9.4) years and disease duration of 6.0 (4.0, 9.3) months. The MDA5-negative group included 36 cases (14 male, 22 female), with an age of onset of 4.9 (2.0, 7.0) years and disease duration of 5.0 (1.8, 7.0) months. The MDA5-positive group exhibited significantly higher rates of arthritis, skin ulcers, and interstitial lung disease (ILD), along with elevated serum ferritin (SF) and erythrocyte sedimentation rate levels compared to the MDA5-negative group (9/18 vs. 11% (4/36), 6/18 vs. 3% (1/36), 16/18 vs. 33% (12/36), 327 (141, 518) vs. 131 (68, 257) μg/L, 17.5 (12.5, 26.8) vs. 11.0 (5.0, 13.0) mm/1 h, χ2=7.92, 7.41, 14.84, Z=2.50, 2.87, all P<0.05). Conversely, the MDA5-positive group had lower rates of muscle weakness and lower creatine kinase levels (5/18 vs. 75% (27/36), 58.5 (49.3, 97.5) vs. 225.0 (68.0, 695.5) U/L, χ2=11.08, Z=-2.94, both P<0.05). Severe pulmonary involvement 6 cases and non-severe pulmonary involvement subgroups 12 cases. Among the MDA5-positive patients, those in the severe pulmonary involvement subgroup had an older age at onset and higher rates of muscle weakness as well as hydroxybutyrate dehydrogenase (HBDH), lactate dehydrogenase (LDH), SF, and Krebs von den Lungen-6 (KL-6) compared to the non-severe subgroup (all P<0.05). In the MDA5-positive group, 17 patients improved after treatment with glucocorticoids combined with immunosuppressants, while One died due to rapidly progressive ILD. Conclusions:Anti-MDA5 antibody-positive JDM is characterized by typical skin rashes, a high incidence of arthritis and skin ulcers, relatively mild muscle involvement, but is prone to ILD. Among MDA5-positive patients, those with older age at onset, muscle involvement (manifested as muscle weakness and elevated muscle enzymes (LDH, HBDH)), or significantly elevated KL-6 and SF levels are more likely to develop severe pulmonary complications.

Parkinson disease(PD)is one of the most prevalent neurodegenerative disorders,typically affecting the elderly.The pathogenesis of PD is intricate and involves multiple factors,with mitochondrial dysfunction recognized as a significant mechanism contributing to its development.MicroRNAs(miRNAs)play a crucial role in the onset and pro-gression of PD.This review focuses on recent research concerning miRNAs associated with PD,particularly their impact on mitochondrial dysfunction.The insights gathered here aim to support the early diagnosis and targeted treatment of PD.

Objective To compare the segmenting efficacy of automatic segmentation models for advanced gastric cancer(AGC)on CT virtual monoenergetic images(VMI),non-linear blending images(NLBI)and mixed-energy images(MEI)based on 3D-nnU-Net.Methods Totally 216 cases of AGC were retrospectively enrolled,among them 185 cases were used to construct,train and validate models and divided into training set(n=154)and test set(n=31)at the ratio of 5∶1,while the other 31 cases were used as validation set to evaluate the generalization of the models.The 70 keV energy level VMI(VMI70 keV),NLBI and MEI were reconstructed with whole-abdominal dual-energy mode venous CT,and automatic segmentation models of AGC,including VMI70 keV,NLBI and MEI models were constructed using 3D-nnU-Net,respectively.Taken manually segmented results as golden standards,the efficacy of each model for segmenting all lesions and T2 stage lesions in test set and validation set were evaluated using Dice similarity coefficient(DSC),intersection over union(IoU)and average symmetric surface distance(ASSD).Results For all lesions in test and validation sets,DSC of 3 models were all＞0.80.DSC and IoU of VMI70 keV and NLBI models were both higher,while their ASSD was lower than those of MEI model(all P＜0.05).For T2 stage AGC in both test set and validation set(each n=5),DSC of MEI model was lower than that of VMI70 keV and NLBI models(both P＜0.05),while IoU of MEI model was lower than that of VMI70 keV model(P＜0.05),and its ASSD was higher than that of NLBI model(P＜0.05).Conclusion All 3D-nnU-Net-based VMI70 keV,NLBI and MEI models could effectively segment AGC on dual-energy CT images,and the segmentation efficacy of the former two were better.

Objective To compare the segmenting efficacy of automatic segmentation models for advanced gastric cancer(AGC)on CT virtual monoenergetic images(VMI),non-linear blending images(NLBI)and mixed-energy images(MEI)based on 3D-nnU-Net.Methods Totally 216 cases of AGC were retrospectively enrolled,among them 185 cases were used to construct,train and validate models and divided into training set(n=154)and test set(n=31)at the ratio of 5∶1,while the other 31 cases were used as validation set to evaluate the generalization of the models.The 70 keV energy level VMI(VMI70 keV),NLBI and MEI were reconstructed with whole-abdominal dual-energy mode venous CT,and automatic segmentation models of AGC,including VMI70 keV,NLBI and MEI models were constructed using 3D-nnU-Net,respectively.Taken manually segmented results as golden standards,the efficacy of each model for segmenting all lesions and T2 stage lesions in test set and validation set were evaluated using Dice similarity coefficient(DSC),intersection over union(IoU)and average symmetric surface distance(ASSD).Results For all lesions in test and validation sets,DSC of 3 models were all＞0.80.DSC and IoU of VMI70 keV and NLBI models were both higher,while their ASSD was lower than those of MEI model(all P＜0.05).For T2 stage AGC in both test set and validation set(each n=5),DSC of MEI model was lower than that of VMI70 keV and NLBI models(both P＜0.05),while IoU of MEI model was lower than that of VMI70 keV model(P＜0.05),and its ASSD was higher than that of NLBI model(P＜0.05).Conclusion All 3D-nnU-Net-based VMI70 keV,NLBI and MEI models could effectively segment AGC on dual-energy CT images,and the segmentation efficacy of the former two were better.