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"Jing, Wei-wei"
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A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery
ObjectivesTo develop and validate a radiomics nomogram to preoperative prediction of isocitrate dehydrogenase (IDH) genotype for astrocytomas, which might contribute to the pretreatment decision-making and prognosis evaluating.MethodsOne hundred five astrocytomas (Grades II–IV) with contrast-enhanced T1-weighted imaging (CE-T1WI), T2 fluid-attenuated inversion recovery (T2FLAIR), and apparent diffusion coefficient (ADC) map were enrolled in this study (training cohort: n = 74; validation cohort: n = 31). IDH1/2 genotypes were determined using Sanger sequencing. A total of 3882 radiomics features were extracted. Support vector machine algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinico-radiological risk factors, the radiomics nomogram was developed. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess these models. Kaplan–Meier survival analysis and log rank test were performed to assess the prognostic value of the radiomics nomogram.ResultsThe radiomics signature was built by six selected radiomics features and yielded AUC values of 0.901 and 0.888 in the training and validation cohorts. The radiomics nomogram based on the radiomics signature and age performed better than the clinico-radiological model (training cohort, AUC = 0.913 and 0.817; validation cohort, AUC = 0.900 and 0.804). Additionally, the survival analysis showed that prognostic values of the radiomics nomogram and IDH genotype were similar (log rank test, p < 0.001; C-index = 0.762 and 0.687; z-score test, p = 0.062).ConclusionsThe radiomics nomogram might be a useful supporting tool for the preoperative prediction of IDH genotype for astrocytoma, which could aid pretreatment decision-making.Key Points• The radiomics signature based on multiparametric and multiregional MRI images could predict IDH genotype of Grades II–IV astrocytomas.• The radiomics nomogram performed better than the clinico-radiological model, and it might be an easy-to-use supporting tool for IDH genotype prediction.• The prognostic value of the radiomics nomogram was similar with that of the IDH genotype, which might contribute to prognosis evaluating.
Journal Article
Motivating role of type H vessels in bone regeneration
Coupling between angiogenesis and osteogenesis has an important role in both normal bone injury repair and successful application of tissue‐engineered bone for bone defect repair. Type H blood vessels are specialized microvascular components that are closely related to the speed of bone healing. Interactions between type H endothelial cells and osteoblasts, and high expression of CD31 and EMCN render the environment surrounding these blood vessels rich in factors conducive to osteogenesis and promote the coupling of angiogenesis and osteogenesis. Type H vessels are mainly distributed in the metaphysis of bone and densely surrounded by Runx2+ and Osterix+ osteoprogenitors. Several other factors, including hypoxia‐inducible factor‐1α, Notch, platelet‐derived growth factor type BB, and slit guidance ligand 3 are involved in the coupling of type H vessel formation and osteogenesis. In this review, we summarize the identification and distribution of type H vessels and describe the mechanism for type H vessel‐mediated modulation of osteogenesis. Type H vessels provide new insights for detection of the molecular and cellular mechanisms that underlie the crosstalk between angiogenesis and osteogenesis. As a result, more feasible therapeutic approaches for treatment of bone defects by targeting type H vessels may be applied in the future. Type H vessels are mainly distributed in the metaphyseal region and sub‐periosteum and show strong positive staining for CD31 and EMCN. They are specialized microvascular components and closely related to bone healing speed through the crosstalk between angiogenesis and osteogenesis.
Journal Article
Pathologic responses and surgical outcomes after neoadjuvant immunochemotherapy versus neoadjuvant chemoradiotherapy in patients with locally advanced esophageal squamous cell carcinoma
BackgroundCurrently, the role of immunotherapy in neoadjuvant setting for patients with locally advanced esophageal squamous cell carcinoma (ESCC) is gradually attracting attention. Few studies compared the efficacy of neoadjuvant immunochemotherapy (NICT) and neoadjuvant chemoradiotherapy (NCRT). Our study aimed to compare treatment response and postoperative complications after NICT followed by surgery with that after conventional NCRT in patients with locally advanced ESCC.MethodsOf 468 patients with locally advanced ESCC, 154 received conventional NCRT, whereas 314 received NICT. Treatment response, postoperative complications and mortality between two groups were compared. Pathological response of primary tumor was evaluated using the Mandard tumor regression grade (TRG) scoring system. Pathological complete response (pCR) of metastatic lymph nodes (LNs) was defined as no viable tumor cell within all resected metastatic LNs. According to regression directionality, tumor regression pattern was summarized into four categories: type I, regression toward the lumen; type II, regression toward the invasive front; type III, concentric regression; and type IV, scattered regression. Inverse probability propensity score weighting was performed to minimize the influence of confounding factors.ResultsAfter adjusting for baseline characteristics, the R0 resection rates (90.9% vs. 89.0%, P=0.302) and pCR (ypT0N0) rates (29.8% vs. 34.0%, P=0.167) were comparable between two groups. Patients receiving NCRT showed lower TRG score (P<0.001) and higher major pathological response (MPR) rate (64.7% vs. 53.6%, P=0.001) compared to those receiving NICT. However, NICT brought a higher pCR rate of metastatic LNs than conventional NCRT (53.9% vs. 37.1%, P<0.001). The rates of type I/II/III/IV regression patterns were 44.6%, 6.8%, 11.4% and 37.1% in the NICT group, 16.9%, 8.2%, 18.3% and 56.6% in the NCRT group, indicating a significant difference (P<0.001). Moreover, there were no significant differences in the incidence of total postoperative complications (35.8% vs. 39.9%, P=0.189) and 30-d mortality (0.0% vs. 1.1%, P=0.062).ConclusionFor patients with locally advanced ESCC, NICT showed a R0 resection rate and pCR (ypT0N0) rate comparable to conventional NCRT, without increased incidence of postoperative complications and mortality. Notablely, NICT followed by surgery might bring a promising treatment response of metastatic LNs.
Journal Article
Highly stabilized and efficient thermoelectric copper selenide
The liquid-like feature of thermoelectric superionic conductors is a double-edged sword: the long-range migration of ions hinders the phonon transport, but their directional segregation greatly impairs the service stability. We report the synergetic enhancement in figure of merit (ZT) and stability in Cu
1.99
Se-based superionic conductors enabled by ion confinement effects. Guided by density functional theory and nudged elastic band simulations, we elevated the activation energy to restrict ion migrations through a cation–anion co-doping strategy. We reduced the carrier concentration without sacrificing the low thermal conductivity, obtaining a ZT of ∼3.0 at 1,050 K. Notably, the fabricated device module maintained a high conversion efficiency of up to ∼13.4% for a temperature difference of 518 K without obvious degradation after 120 cycles. Our work could be generalized to develop electrically and thermally robust functional materials with ionic migration characteristics.
Cu
2
Se is of interest for thermoelectrics as it is environmentally sustainable and has a high figure of merit ZT; however, copper ion migration impacts device stability. Here a co-doping strategy that combines steric and electrostatic effects is shown to improve device stability as well as improving
ZT
to 3.
Journal Article
Wide-temperature-range thermoelectric n-type Mg3(Sb,Bi)2 with high average and peak zT values
Mg
3
(Sb,Bi)
2
is a promising thermoelectric material suited for electronic cooling, but there is still room to optimize its low-temperature performance. This work realizes >200% enhancement in room-temperature
zT
by incorporating metallic inclusions (Nb or Ta) into the Mg
3
(Sb,Bi)
2
-based matrix. The electrical conductivity is boosted in the range of 300–450 K, whereas the corresponding Seebeck coefficients remain unchanged, leading to an exceptionally high room-temperature power factor >30 μW cm
−1
K
−2
; such an unusual effect originates mainly from the modified interfacial barriers. The reduced interfacial barriers are conducive to carrier transport at low and high temperatures. Furthermore, benefiting from the reduced lattice thermal conductivity, a record-high average
zT
> 1.5 and a maximum
zT
of 2.04 at 798 K are achieved, resulting in a high thermoelectric conversion efficiency of 15%. This work demonstrates an efficient nanocomposite strategy to enhance the wide-temperature-range thermoelectric performance of n-type Mg
3
(Sb,Bi)
2
, broadening their potential for practical applications.
The utilization of Mg
3
(Sb,Bi)
2
in thermoelectric devices is hindered by its low performance near room temperature. Here, authors report thermoelectric performance enhancement of Mg
3
(Sb,Bi)
2
within a wide temperature range by incorporating metallic inclusions at grain boundaries. (279 in total)
Journal Article
Investigating the Mechanism of Hyperglycemia-Induced Fetal Cardiac Hypertrophy
Hyperglycemia in diabetic mothers enhances the risk of fetal cardiac hypertrophy during gestation. However, the mechanism of high-glucose-induced cardiac hypertrophy is not largely understood. In this study, we first demonstrated that the incidence rate of cardiac hypertrophy dramatically increased in fetuses of diabetic mothers using color ultrasound examination. In addition, human fetal cardiac hypertrophy was successfully mimicked in a streptozotocin (STZ)-induced diabetes mouse model, in which mouse cardiac hypertrophy was diagnosed using type-M ultrasound and a histological assay. PH3 immunofluorescent staining of mouse fetal hearts and in vitro-cultured H9c2 cells indicated that cell proliferation decreased in E18.5, E15.5 and E13.5 mice, and cell apoptosis in H9c2 cells increased in the presence of high glucose in a dose-dependent manner. Next, we found that the individual cardiomyocyte size increased in pre-gestational diabetes mellitus mice and in response to high glucose exposure. Meanwhile, the expression of β-MHC and BMP-10 was up-regulated. Nkx2.5 immunofluorescent staining showed that the expression of Nkx2.5, a crucial cardiac transcription factor, was suppressed in the ventricular septum, left ventricular wall and right ventricular wall of E18.5, E15.5 and E13.5 mouse hearts. However, cardiac hypertrophy did not morphologically occur in E13.5 mouse hearts. In cultured H9c2 cells exposed to high glucose, Nkx2.5 expression decreased, as detected by both immunostaining and western blotting, and the expression of KCNE1 and Cx43 was also restricted. Taken together, alterations in cell size rather than cell proliferation or apoptosis are responsible for hyperglycemia-induced fetal cardiac hypertrophy. The aberrant expression of Nkx2.5 and its regulatory target genes in the presence of high glucose could be a principal component of pathogenesis in the development of fetal cardiac hypertrophy.
Journal Article
Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer
Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation. Results: The EPLA model achieved an area-under-the-curve (AUC) of 0.8848 (95% CI: 0.8185-0.9512) in the TCGA-COAD test set and an AUC of 0.8504 (95% CI: 0.7591-0.9323) in the external validation set Asian-CRC after transfer learning. Notably, EPLA captured the relationship between pathological phenotype of poor differentiation and MSI (P < 0.001). Furthermore, the five pathological imaging signatures identified from the EPLA model were associated with mutation burden and DNA damage repair related genotype in the genomic profiles, and antitumor immunity activated pathway in the transcriptomic profiles. Conclusions: Our pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort. The interpretability of our model by association with pathological, genomic and transcriptomic phenotypes lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy.
Journal Article
Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda
Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.
Journal Article