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Intravenous thrombolysis with recombinant tissue plasminogen activator (rtPA) is the primary treatment for ischemic stroke. However, rtPA treatment can substantially increase blood‐brain barrier (BBB) permeability and susceptibility to hemorrhagic transformation. Herein, the mechanism underlying the side effects of rtPA treatment is investigated and demonstrated that ferroptosis plays an important role. The ferroptosis inhibitor, liproxstatin‐1 (Lip) is proposed to alleviate the side effects. A well‐designed macrocyclic carrier, glucose‐modified azocalix[4]arene (GluAC4A), is prepared to deliver Lip to the ischemic site. GluAC4A bound tightly to Lip and markedly improved its solubility. Glucose, modified at the upper rim of GluAC4A, imparts BBB targeting to the drug delivery system owing to the presence of glucose transporter 1 on the BBB surface. The responsiveness of GluAC4A to hypoxia due to the presence of azo groups enabled the targeted release of Lip at the ischemic site. GluAC4A successfully improved drug accumulation in the brain, and Lip@GluAC4A significantly reduced ferroptosis, BBB leakage, and neurological deficits induced by rtPA in vivo. These findings deepen the understanding of the side effects of rtPA treatment and provide a novel strategy for their effective mitigation, which is of great significance for the treatment and prognosis of patients with ischemic stroke. [ABSTRACT FROM AUTHOR]

Background Glioma is a kind of intracranial space⁃occupying lesion, which causes heavy disease burden. For glioma patients, early accurate diagnosis and early treatment could effectively prolong the progression free survival. Suspected glioma patients would be firstly examined by MRI for diagnosis. The MRI images were mainly read by radiologists artificially. The diagnosis by different radiologists might be different, and the heavy workload might cause a decline in reading efficiency. In recent years, it is becoming possible to use deep learning technology for medical image recognition and diagnosis. This study used machine learning algorithms related to artificial neural network (ANN) to assist image practitioner in reading the head MRI images of patients with glioma, which may cause labor⁃saving, and could increase the efficiency of reading images and reduce the different results cause by different personal experiencse. Methods These images were from The Cancer Imaging Archive (TCIA) database. Format of these images was DICOM. And they were from 130 adult glioma cases, a total of 40 036 copies. These images were randomly split as training set (28 025 copies) and test set (12 011 copies). Then in each set, images were split as "tumor image" and "normal image" according to medical experts' annotation. ZFNet model, a type of convolutional neural network, was used to build image recognition and classification model. The reinforcement learning curve was draw to observe the trend of accuracy of training changed with the training steps. Put the test set into the model, the overall classification accuracy of all MRI images, the positive predictive value, sensitivity, specificity and F1 ⁃ measure of the tumor images were calculated. At the same time, AlexNet was also used to build a same model to compare with the ZFNet model by the prediction indexes of the classification ability of MRI images (the prediction ability of diagnosis of glioma). Results The training accuracy of ZFNet model was 99.7% after 38 757 steps and of the AlexNet model was 98.23% after 37 984 steps. After testing, the image prediction accuracy of all MRI images of ZFNet model was 84.42% (10 140/12 011), the positive predictive value of prediction of "tumor image" was 80.77% (4817/5964), the sensitivity was 86.93% (4817/5541), the specificity was 82.27% (5323/6470), and the F1⁃measure was 83.74%. The above indexes of AlexNet model were 80.74% (9698/12 011), 77.68% (4529/5830), 81.74% (4529/5541), 79.89% (5169/6470) and 79.66%, respectively. The classification performances of ZFNet model were satisfied and were superior to AlexNet model in each dimension. Conclusions ZFNet model has a good prediction performance ability in glioma brain MRI image classification, and it is able to provide a good technical support for establishing a glioma image aided diagnosis model. doi：10.3969/j.issn.1672⁃6731.2021.03.006

A keloid is the process of skin healing, collagen synthesis and metabolism of the loss of normal control in a sustained hyperactive state, resulting in excessive proliferation of collagen fibers. A large-scale genome-wide association study (GWAS) has identified multiple single nucleotide polymorphisms (SNPs) in the 3q22.3 loci that are associated with keloids in a Japanese population. However, the associations of SNPs in 3q22.3 with keloids were not confirmed in a selected Chinese population by a replication study. Thus, in the present study, the relationships between keloids and 3q22.3 were assessed in another independent Chinese Han population, including 309 keloid patients and 1080 control subjects. The results displayed that rs940187 was associated with keloids (OR=1.88, 95% CI 1.27-2.78, P=1.35E -3 ) and remained significant after Bonferroni's correction for multiple testing, while rs1511412 showed only a trend association (OR=2.23, 95% CI 1.09-4.55, P=0.02) with keloids. In addition, we subsequently checked the annotation datasets for rs940187 with eQTLs and obtained two hits, trans-proteins SLC7A9 and LEMD3, with significant P values less than 1e-4. In summary, genomic risk variants at 3q22.3 are associated with keloids in a Chinese Han population and contribute to the development and deterioration of the keloids, together with environmental factors., Competing Interests: None.

Keloids are abnormally raised fibroproliferative lesions that usually occur following cutaneous traumas. Recently, a large-scale genome-wide association study (GWAS) has identified multiple single nucleotide polymorphisms (SNPs) in three genetic loci that are associated with keloids in Japanese population. Subsequently, two reported loci 1q41 (rs873549 and rs1442440) and 15q21.3 (rs2271289) for keloids were confirmed in selected Chinese population. The association of these SNPs with clinical features of keloids, has not yet been studied. To explore the role of these SNPs in the pathogenesis of keloids, we performed a case-controlled study in another independent Chinese Han population to analyze the correlation between 4 SNPs (rs873549, rs2118610, rs1511412, rs2271289) and keloids phenotypes. 309 keloids patients and 1080 control subjects were included. The results showed that, in the dominant mode of inheritance, the minor allele T of SNP rs2271289 had significantly higher odd ratios (ORs) in the severe keloid group compared with both the controls and the mild keloid group. The ORs were maintained after Bonferroni's correction (OR: 4.09, 95% CI: 1.78-9.37, P-value 3.25E-04). The ratio of the severe: mild OR for rs2271289 (dominant model) is (4.73/1.84=2.57). Similar associations in SNP rs2271289 were seen for groups with no family history and multiplesite compared with the control groups. No associations between keloid number, family history or severity relative to the controls were observed for the other three SNPs. Our data support that rs2271289 is strongly associated with severe keloids and might contribute to the complexity of clinical features of keloids.

Disseminated superficial actinic porokeratosis (DSAP) is the most common form of porokeratosis and a severe chronic autosomal dominant cutaneous disorder with high genetic heterogeneity. Recently, the mevalonate kinase (MVK) gene has been identified as a candidate gene responsible for DSAP and multiple mutations have been reported. Here, we report identification of a novel missense mutation in the MVK gene in a Chinese family with DSAP. A 50-year-old male was diagnosed as proband of DSAP based on the clinical and histological findings, which show numerous hyperpigmented macules by physical examination and cornoid lamella by skin biopsy. Similar skin symptoms were also observed in his father, who died many years ago. We prepared genomic DNA from the proband, unaffected individuals from his family members, as well as 100 unrelated healthy controls. PCR was then conducted using the above genomic DNA as template and the MVK gene-specific primers. The PCR product was subjected to direct sequencing and the sequence was compared to that of MVK gene within the NCBI database. We detected a heterozygous C to G transition at nucleotide 643 in exon 7 of MVK gene of the proband. This will result in an amino acid change at codon 215 (P.Arg215Gly.), which is from an arginine codon (CGA) to a Glycine codon (GGA). We did not detect any mutation in the unaffected family members or the 100 unrelated healthy controls, demonstrating that this is a novel missense mutation in MVK gene and therefore, contributes to the molecular diagnosis of DSAP.