A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma.

Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathologists. Here we sought to develop and validate a deep learning model capable of precisely distinguishing PCNSL from non-PCNSL lesions, especially glioma, using hematoxylin and eosin (H&E)-stained frozen whole-slide images. Also, we compared its performance against pathologists of varying expertise. Additionally, a human-machine fusion approach integrated both model and pathologic diagnostics. In external cohorts, LGNet achieved AUROCs of 0.965 and 0.972 in distinguishing PCNSL from glioma and AUROCs of 0.981 and 0.993 in differentiating PCNSL from non-PCNSL lesions. Outperforming several pathologists, LGNet significantly improved diagnostic performance, further augmented to some extent by fusion approach. LGNet's proficiency in frozen section analysis and its synergy with pathologists indicate its valuable role in intraoperative diagnosis, particularly in discriminating PCNSL from glioma, alongside other lesions.

Report of a magpie preying on a post-fledgling Daurian redstart.

A magpie (Pica pica) preying on a fledgling of Daurian redstart (Phoenicurus auroreus) was incidentally recorded with a video shot by mobile phone on 26 May 2021, providing direct evidence for magpie predation. It also shows that predation is an important factor that affects the survival of fledglings, indicating that survival of fledglings should be considered in evaluating breeding success of birds. The fledgling was about 13-day-old posthatching, and it was on its first day of leaving the nest when the incident occurred. It was preyed upon by a magpie 10 m away from the nest by two attempts under strong defensive behaviour from the female.

Identification of Potential Prognostic Biomarkers Associated With Cancerometastasis in Skin Cutaneous Melanoma.

Skin cutaneous melanoma (SKCM) is a highly aggressive tumor. The mortality and drug resistance among it are high. Thus, exploring predictive biomarkers for prognosis has become a priority. We aimed to find immune cell-based biomarkers for survival prediction. Here 321 genes were differentially expressed in immune-related groups after ESTIMATE analysis and differential analysis. Two hundred nineteen of them were associated with the metastasis of SKCM via weighted gene co-expression network analysis. Twenty-six genes in this module were hub genes. Twelve of the 26 genes were related to overall survival in SKCM patients. After a multivariable Cox regression analysis, we obtained six of these genes (PLA2G2D, IKZF3, MS4A1, ZC3H12D, FCRL3, and P2RY10) that were independent prognostic signatures, and a survival model of them performed excellent predictive efficacy. The results revealed several essential genes that may act as significant prognostic factors of SKCM, which could deepen our understanding of the metastatic mechanisms and improve cancer treatment.

m6A-Related lncRNAs Are Potential Biomarkers for the Prognosis of Metastatic Skin Cutaneous Melanoma.

Background: The incidence of skin cutaneous melanoma (SKCM) has risen more rapidly than any other solid tumor in the past few decades. The median survival for metastatic melanoma is only six to nine months and the 5°years survival rate of patients with conventional therapy is less than 5%. Our aim was to reveal the potential molecular mechanism in m6A modification of lncRNA and provide candidate prognostic biomarkers for metastatic SKCM. Methods: lncRNAs expression level was obtained by re-annotation in TCGA and CCLE datasets. m6A-related lncRNAs were selected though correlation analysis. Univariate cox regression analysis was used to screen out independent prognostic factors. LASSO Cox regression was performed to construct an m6A-related lncRNA model (m6A-LncM). Univariate survival analysis and ROC curve were used to assess the prognostic efficacy of this model and candidate lncRNAs. Enrichment analysis was used to explore the candidate genes' functions. Results: We obtained 1,086 common m6A-related lncRNAs after Pearson correlation analysis in both two datasets. 130 out of the 1,086 lncRNAs are independent prognostic factors. 24 crucial lncRNAs were filtered after LASSO Cox regression analysis. All the m6A-LncM and the 24 lncRNAs were related to overall survival. Stratified survival analysis of m6A-LncM showed that the model retains its prognostic efficacy in recurrence, radiation therapy and other subgroups. Enrichment analysis also found that these lncRNAs were immune associated. Conclusion: Here, we obtained 24 crucial lncRNAs that may be potential biomarkers to predict survival of metastatic SKCM and may provide a new insight to improve the prognosis of it.

CT texture analysis in histological classification of epithelial ovarian carcinoma.

OBJECTIVES: The study aimed to compare the ability of morphological and texture features derived from contrast-enhanced CT in histological subtyping of epithelial ovarian carcinoma (EOC). METHODS: Consecutive 205 patients with newly diagnosed EOC who underwent contrast-enhanced CT were included and dichotomised into high-grade serous carcinoma (HGSC) and non-HGSC. Clinical information including age and cancer antigen 125 (CA-125) was documented. The pre-treatment images were analysed using commercial software, TexRAD, by two independent radiologists. Eight qualitative CT morphological features were evaluated, and 36 CT texture features at 6 spatial scale factors (SSFs) were extracted per patient. Features' reduction was based on kappa score, intra-class correlation coefficient (ICC), univariate ROC analysis and Pearson's correlation test. Texture features with ICC ≥ 0.8 were compared by histological subtypes. Patients were randomly divided into training and testing sets by 8:2. Two random forest classifiers were determined and compared: model 1 incorporating selected morphological and clinical features and model 2 incorporating selected texture and clinical features. RESULTS: HGSC showed specifically higher texture features than non-HGSC (p < 0.05). Both models performed highly in predicting histological subtypes of EOC (model 1: AUC 0.891 and model 2: AUC 0.937), and no statistical significance was found between the two models (p = 0.464). CONCLUSION: CT texture analysis provides objective and quantitative metrics on tumour characteristics with HGSC demonstrating specifically high texture features. The model incorporating texture analysis could classify histology subtypes of EOC with high accuracy and performed as well as morphological features. KEY POINTS: • A number of CT morphological and texture features showed good inter- and intra-observer agreements. • High-grade serous ovarian carcinoma showed specifically higher CT texture features than non-high-grade serous ovarian carcinoma. • CT texture analysis could differentiate histological subtypes of epithelial ovarian carcinoma with high accuracy.

High expression of eIF4A2 is associated with a poor prognosis in esophageal squamous cell carcinoma.

Eukaryotic initiation factor 4A-II (eIF4A2) is an ATP-dependent RNA helicase involved in mRNA translation. Abnormal expression of eIF4A2 has been reported as a prognostic factor in different types of cancer. However, little is known regarding the function of eIF4A2 in esophageal squamous cell carcinoma (ESCC). In the present study, 253 samples were collected from patients diagnosed with ESCC, and the expression of eIF4A2 was detected by immunohistochemical staining. The clinicopathological and prognostic significance of eIF4A2 expression in ESCC were then statistically analyzed. The results demonstrated that eIF4A2 was specifically localized to the cytoplasm. Kaplan-Meier analysis also revealed that eIF4A2 expression was associated with the clinical prognosis of patients with ESCC. The median disease-free and overall survival times were 40 and 48 months for patients with low eIF4A2 expression, compared with 16 and 25 months in the high eIF4A2 expression group, respectively. In conclusion, high expression levels of eIF4A2 are associated with a poor prognosis and may be used as a potential prognostic indicator in patients with ESCC.

Identification of a novel mutation of the NTRK1 gene in patients with congenital insensitivity to pain with anhidrosis (CIPA).

INTRODUCTION: Congenital insensitivity to pain with anhidrosis (CIPA) is a rare autosomal recessive disorder resulting from NTRK1 mutation. Over 105 NTRK1 mutations have been reported in CIPA patients worldwide. The causative NTRK1 mutations lead to loss of function of the TrkA protein, an important ligand for nerve growth factor (NGF), and therefore induce various clinical phenotypes associated with neuron maturation defects. MATERIALS AND METHODS: Three patients from unrelated families with CIPA were subjected to detailed clinical examinations. Blood samples were collected from all the patients and their available family members, as well as 200 healthy volunteers. Sanger sequencing for all the exons and splicing sites of NTRK1 was performed on all samples. The phenotype-genotype relationship and genetic epidemiology of Chinese CIPA patients were also analysed. RESULTS: A total of four different NTRK1 mutations [c.851-33T>A, c.44G>A (p.Trp15*), c.287+2dupT, c.1549G>C (p.Gly517Arg)] were identified in these families, and c.1549G>C (p.Gly517Arg) was a novel mutation that had not been reported previously. The 'mild' manifestations observed in patients with c.851-33T>A indicated this mutation as a 'mild' mutation. After reviewing studies reporting mutations in Chinese CIPA patients, we speculate the mutation c.851-33T>A is one of the founder mutations in the Chinese population. CONCLUSIONS: Our research expanded the spectrum of the NTRK1 mutations associated with CIPA patients, provided additional clues relating to the phenotype-genotype relationship in CIPA, and summarized the features of the genetic epidemiology of CIPA in the Chinese ethnic group.

INHBA upregulation correlates with poorer prognosis in patients with esophageal squamous cell carcinoma.

PURPOSE: INHBA, which encodes a member of the TGF-beta superfamily of proteins, has been identified to play a critical role in different types of cancer. However, its clinical significance in esophageal squamous cell carcinoma (ESCC) has never been reported. PATIENTS AND METHODS: In this study, we collected 239 ESCC paraffin-embedded specimens and measured the expression of INHBA with immunohistochemistry (IHC). The clinical and prognostic significance of INHBA expression was statistically analyzed. What is more, we conducted a meta-analysis to study the prognostic value of INHBA expression in multiple types of solid tumors. RESULTS: The results showed that INHBA expression was observed predominantly in the cytoplasm of cells in the ESCC specimens. INHBA expression was closely correlated with N categories (P=0.026). Kaplan-Meier analysis showed that ESCC patients in the low INHBA expression subgroup had significantly better prognosis than those with high INHBA level. Subgroup analysis revealed that INHBA distinguished the disease-free survival (DFS) and overall survival (OS) when patients were stratified by TNM stage status and N status. Multivariate analysis results suggested that INHBA expression was an independent factor that affected OS (HR =1.679, P=0.022) and DFS (HR =1.715, P=0.017). In the meta-analysis, six papers with 1321 patients were included and patients with high INHBA level had worse prognosis than patients with low INHBA level (HR 2.50, 95% CI 1.75-3.57, P<0.0001). CONCLUSION: High INHBA level predicts poor prognosis in ESCC and other solid tumors. More studies are required to elucidate the role of INHBA and its clinical application in cancer settings.

Optically controlled reversible protein hydrogels based on photoswitchable fluorescent protein Dronpa.

Exploiting the optically controlled association and dissociation behavior of a photoswitchable fluorescent protein, Dronpa145N, here we demonstrate the engineering of an optically switchable reversible protein hydrogel using Dronpa145N-based protein building blocks. Our results open the possibility to optically tune the mechanical, chemical and structural properties of protein hydrogels.

Decorating a Blank Slate Protein Hydrogel: A General and Robust Approach for Functionalizing Protein Hydrogels.

Protein hydrogels constructed from recombinant proteins have attracted increasing interests for fundamental biological studies as well as applications in biomedical engineering field. In such protein hydrogels, biochemical and physical properties of protein hydrogels are often coupled to each other, making it challenging to investigate the individual effect of chemical and physical cues on cells. Moreover, laborious engineering is often required to incorporate different protein ligands into such hydrogels. To address these challenges, functionalizing a blank slate protein hydrogel is an attractive approach. However, conjugating ligands to such a blank slate protein hydrogel is challenging, as existing bioconjugation methods developed in synthetic polymer hydrogels cannot be readily adapted for protein hydrogels, significantly impeding the use of this approach in the field. Here we report a facile, general, and robust method, which is based on the SpyCatcher-SpyTag chemistry, to covalently functionalize the "blank slate" of protein hydrogels using genetically encoded interacting partners. We demonstrate that this novel method enables covalent conjugation of a wide variety of ligands, including full-length intact folded proteins, to a blank slate protein hydrogel, and allows for the decoupling of biochemical and physical properties of hydrogels from each other and investigating the individual effect of biochemical and mechanical cues on cell behaviors. To our best knowledge, this is the first general approach enabling functionalization of protein hydrogels, and we anticipate that this novel approach will find a broad range of uses in protein-based biomaterials for applications in biomedical engineering.