NUP155 is a kind of important nucleoporins on the nuclear pore complex which plays an important role in the process of mediating macromolecular substances passing in and out of the nucleus. Primary cardiac arrhythmia is one of the important reasons to lead to sudden death, mainly due to the mutations of the gene which codes ion channel on the myocardial cell membrane, so it's also known as "cardiac ion channel disease".NUP155 is the first found non-ion-channel gene that its mutations can lead to primary cardiac arrhythmias and sudden cardiac death. This article mainly focuses on the structure and biological function of NUP155 and its relationship with Primary arrhythmic sudden cardiac death.

D ue to the negative autopsy and w ithout cardiac structural abnorm alities, unexpected sudden cardiac death (U SC D ) is alw ays a tough issue for forensic pathological expertise. U SC D m ay be asso-ciated w ith parts of fatal arrhythm ic diseases. T hese arrhythm ic diseases m ay be caused by disorders of cardiac ion channels or channel-related proteins. C aveolin can com bine w ith m ultiple m yocardial ion channel proteins through its scaffolding regions and plays an im portant role in m aintaining the depolar-ization and repolarization of cardiac action potential. W hen the structure and function of caveolin are af-fected by gene m utations or abnorm al protein expression, the functions of the regulated ion channels are correspondingly im paired, w hich leads to the occurrence of m ultiple channelopathies, arrhythm ia or even sudden cardiac death. It is im portant to study the effects of caveolin on the functions of ion channels for exploring the m echanism s of m alignant arrhythm ia and sudden cardiac death.

Objective T o explore the genetic variation sites of caveolin (C A V ) and their correlation w ith sudden unexplained death (SU D ).Methods The blood sam ples w ere collected from SU D group (71 cases), coronary artery disease (C A D ) group (62 cases) and control group (60 cases), respectively. T he genom e D N A w ere extracted and sequencing w as perform ed directly by am plifying gene coding region and exon-intron splicing region of CAV1 and CAV3 using PC R . T he type of heritable variation of CVA w as con-firm ed and statistical analysis w as perform ed. Results A total of 4 variation sites that m aybe significa-tive w ere identified in SU D group, and tw o w ere new found w hich w ere CAV1: c.45C>T (T 15T ) and CAV1:c.512G>A (R 171H ), and tw o w ere SN P loci w hich w ere CAV1:c.246C>T (rs35242077) and CAV3:c.99C>T (rs1008642) and had significant difference (P<0.05) in allele and genotype frequencies betw een SU D and control groups. Forem entioned variation sites w ere not found in C A D group. Conclu-sion T he variants of CAV1 and CAV3 m ay be correlated w ith a part of SU D group.

Objective:To build a predictive model for symptomatic radiation pneumonitis(RP) using the pretreatment CT radiomics features, clinical and dosimetric data of lung cancer patients by using machine learning method.Methods:A retrospective analysis of 103 lung cancer patients who underwent radiotherapy in the Affiliated Hospital of Jiangnan University from November 2018 to April 2020 was performed. Total normal lung tissues were segmented as an interested volume in pretreatment CT images, and then 250 radiomics features were extracted. The correlations of RP and clinical or dosimetric features were firstly investigated with univariate analysis. Then all clinical data, dosimetric data and CT radiomics features were collected and considered as predictors for modeling of RP grade ≥ 2. Features were selected through LASSO machine learning method, and the predictive model was built. Finally, nomogram for risk of RP were obtained according to the selected features.Results:The result of univariate analysis showed that symptomatic RP was significantly correlated with lung dosimetric parameters including mean lung dose (MLD), V20 Gy and V30 Gy( t=2.20, 2.34 and 2.93, P<0.05). Four features, including lung dose volume percentage V30 Gyand three radiomics features, entropy feature of GLCM, mean and median feature of wavelet histogram were selected among all clinical, dosimetric features and radiomics features. AUC of the predicted model obtained from selected features reached 0.757. For convenient clinical use, the nomogram were obtained, and then personalized RP risk prediction and early intervention could be performed according to this nomogram. Conclusions:Pretreatment CT radiomics and dosimetric features can be used in predicting symptomatic RP, which will be useful for advanced intervention treatment.

It is a challenge to determine the cause of death in cases caused by both injury and disease in forensic pathology examinations.Here,we retrospectively analyzed 140 such cases enrolled in the Sun Yat-sen University Forensic Identification Center from 2013 to 2021.It was found that the cases caused by both injury and disease accounted for 2.89%of the total number of cases during the same period.The male-to-female ratio was 5.09.Cardiovascular disease accounted for 71.43%of the death-leading diseases,and coronary heart disease accounted for 49.29%.Among the three types of injury and disease relationship,the proportion of disease-based and injury-assisted cases accounted for more than half(65.86%),and the proportion of injury and disease with the same effect cases was the least(12.14%).Medical treatment was involved in as more as 82.86%of the cases.Re-identification were carried out in 10.71%of the cases,53.33%of which were with a≤24-hour-survival time,higher than the proportion(20.80%)of this survival time in initial identification cases(x2 = 13.84,P = 0.000 2).This study revealed the epidemiological characteristics of cases caused by both injury and disease,and supplied useful data for improving the level of forensic identification in such cases.