冠心病介入治疗后支架内再狭窄发生率及影响因素

本研究的目的是调查分析冠心病介入治疗后支架内再狭窄发生率及影响因素。采用回顾性研究的方法,选取了2020年至2024年在宁夏人民医院接受冠心病介入治疗的患者作为研究对象,共计400例患者。收集了患者的临床资料、手术方式、术后药物治疗情况等信息,并进行了随访观察。在随访期间,共有36例患者出现了支架内再狭窄的情况,再狭窄发生率为9%。通过对患者资料的统计分析发现,支架植入位置、支架类型、患者年龄、糖尿病史、术后药物依从性等因素与再狭窄的发生率有一定的相关性。由此可见,冠心病介入治疗后支架内再狭窄是一个常见并且容易被忽视的并发症,临床医生应该重视患者的随访观察,并且针对影响因素进行干预,以降低再狭窄的发生率,提高患者的治疗效果和生存质量。

Mechanism of Kinect-based virtual reality training for motor functional recovery of upper limbs after subacute stroke

The Kinect-based virtual reality system for the Xbox 360 enables users to control and interact with the game console without the need to touch a game controller, and provides rehabilitation training for stroke patients with lower limb dysfunctions. However, the underlying mechanism remains un- clear. In this study, 18 healthy subjects and five patients after subacute stroke were included. The five patients were scanned using functional MRI prior to training, 3 weeks after training and at a 12-week follow-up, and then compared with healthy subjects. The FugI-Meyer Assessment and Wolf Motor Function Test scores of the hemiplegic upper limbs of stroke patients were significantly increased 3 weeks after training and at the 12-week follow-up. Functional MRI results showed that contralateral primary sensorimotor cortex was activated after Kinect-based virtual reality training in the stroke patients compared with the healthy subjects. Contralateral primary sensorimotor cortex, the bilateral supplementary motor area and the ipsilateral cerebellum were also activated during hand-clenching in all 18 healthy subjects. Our findings indicate that Kinect-based virtual reality training could promote the recovery of upper limb motor function in subacute stroke patients, and brain reorganization by Kinect-based virtual reality training may be linked to the contralateral sen- sorimotor cortex.

Improved deep learning aided key recovery framework:applications to large-state block ciphers

At the Annual International Cryptology Conference in 2019,Gohr introduced a deep learning based cryptanalysis technique applicable to the reduced-round lightweight block ciphers with a short block of SPECK32/64.One significant challenge left unstudied by Gohr's work is the implementation of key recovery attacks on large-state block ciphers based on deep learning.The purpose of this paper is to present an improved deep learning based framework for recovering keys for large-state block ciphers.First,we propose a key bit sensitivity test(KBST)based on deep learning to divide the key space objectively.Second,we propose a new method for constructing neural distinguisher combinations to improve a deep learning based key recovery framework for large-state block ciphers and demonstrate its rationality and effectiveness from the perspective of cryptanalysis.Under the improved key recovery framework,we train an efficient neural distinguisher combination for each large-state member of SIMON and SPECK and finally carry out a practical key recovery attack on the large-state members of SIMON and SPECK.Furthermore,we propose that the 13-round SIMON64 attack is the most effective approach for practical key recovery to date.Noteworthly,this is the first attempt to propose deep learning based practical key recovery attacks on18-round SIMON128,19-round SIMON128,14-round SIMON96,and 14-round SIMON64.Additionally,we enhance the outcomes of the practical key recovery attack on SPECK large-state members,which amplifies the success rate of the key recovery attack in comparison to existing results.

A deep learning aided differential distinguisher improvement framework with more lightweight and universality

In CRYPTO 2019,Gohr opens up a new direction for cryptanalysis.He successfully applied deep learning to differential cryptanalysis against the NSA block cipher SPECK32/64,achieving higher accuracy than traditional differential distinguishers.Until now,one of the mainstream research directions is increasing the training sample size and utilizing different neural networks to improve the accuracy of neural distinguishers.This conversion mindset may lead to a huge number of parameters,heavy computing load,and a large number of memory in the distinguishers training process.However,in the practical application of cryptanalysis,the applicability of the attacks method in a resourceconstrained environment is very important.Therefore,we focus on the cost optimization and aim to reduce network parameters for differential neural cryptanalysis.ln this paper,we propose two cost-optimized neural distinguisher improvement methods from the aspect of data format and network structure,respectively.Firstly,we obtain a partial output difference neural distinguisher using only 4-bits training data format which is constructed with a new advantage bits search algorithm based on two key improvement conditions.In addition,we perform an interpretability analysis of the new neural distinguishers whose results are mainly reflected in the relationship between the neural distinguishers,truncated differential,and advantage bits.Secondly,we replace the traditional convolution with the depthwise separable convolution to reduce the training cost without affecting the accuracy as much as possible.Overall,the number of training parameters can be reduced by less than 50%by using our new network structure for training neural distinguishers.Finally,we apply the network structure to the partial output difference neural distinguishers.The combinatorial approach have led to a further reduction in the number of parameters(approximately 30% of Gohr's distinguishers for SPECK).

Transcriptome analysis of CD4^(+)T cells from HIV-infected individuals receiving ART with LLV revealed novel transcription factors regulating HIV-1 promoter activity

Some HIV-infected individuals receiving ART develop low-level viremia(LLV),with a plasma viral load of 50-1000 copies/mL.Persistent low-level viremia is associated with subsequent virologic failure.The peripheral blood CD4^(+)T cell pool is a source of LLV.However,the intrinsic characteristics of CD4^(+)T cells in LLV which may contribute to low-level viremia are largely unknown.We analyzed the transcriptome profiling of peripheral blood CD4^(+)T cells from healthy controls(HC)and HIV-infected patients receiving ART with either virologic sup-pression(VS)or LLV.To identify pathways potentially responding to increasing viral loads from HC to VS and to LLV,KEGG pathways of differentially expressed genes(DEGs)were acquired by comparing VS with HC(VS-HC group)and LLV with VS(LLV-VS group),and overlapped pathways were analyzed.Characterization of DEGs in key overlapping pathways showed that CD4^(+)T cells in LLV expressed higher levels of Th1 signature transcription factors(TBX21),toll-like receptors(TLR-4,-6,-7 and-8),anti-HIV entry chemokines(CCL3 and CCL4),and anti-IL-1βfactors(ILRN and IL1R2)compared to VS.Our results also indicated activation of the NF-κB and TNF signaling pathways that could promote HIV-1 transcription.Finally,we evaluated the effects of 4 and 17 tran-scription factors that were upregulated in the VS-HC and LLV-VS groups,respectively,on HIV-1 promoter activity.Functional studies revealed that CXXC5 significantly increased,while SOX5 markedly suppressed HIV-1 tran-scription.In summary,we found that CD4^(+)T cells in LLV displayed a distinct mRNA profiling compared to that in VS,which promoted HIV-1 replication and r+eactivation of viral latency and may eventually contribute to virologic failure in patients with persistent LLV.CXXC5 and SOX5 may serve as targets for the development of latency-reversing agents.