BackgroundOesophageal cancer is one of the most deadly forms of cancer worldwide. Long non-coding RNAs (lncRNAs) are often found to have important regulatory roles.ObjectiveTo assess the lncRNA expression profile of oesophageal squamous cell carcinoma (OSCC) and identify prognosis-related lncRNAs.MethodLncRNA expression profiles were studied by microarray in paired tumour and normal tissues from 119 patients with OSCC and validated by qRT-PCR. The 119 patients were divided randomly into training (n=60) and test (n=59) groups. A prognostic signature was developed from the training group using a random Forest supervised classification algorithm and a nearest shrunken centroid algorithm, then validated in a test group and further, in an independent cohort (n=60). The independence of the signature in survival prediction was evaluated by multivariable Cox regression analysis.ResultsLncRNAs showed significantly altered expression in OSCC tissues. From the training group, we identified a three-lncRNA signature (including the lncRNAs ENST00000435885.1, XLOC_013014 and ENST00000547963.1) which classified the patients into two groups with significantly different overall survival (median survival 19.2 months vs >60 months, p<0.0001). The signature was applied to the test group (median survival 21.5 months vs >60 months, p=0.0030) and independent cohort (median survival 25.8 months vs >48 months, p=0.0187) and showed similar prognostic values in both. Multivariable Cox regression analysis showed that the signature was an independent prognostic factor for patients with OSCC. Stratified analysis suggested that the signature was prognostic within clinical stages.ConclusionsOur results suggest that the three-lncRNA signature is a new biomarker for the prognosis of patients with OSCC, enabling more accurate prediction of survival.

Ligand-induced chirality in semiconductor nanocrystals (NCs) has attracted attention because of the tunable optical properties of the NCs. Induced circular dichroism (CD) has been observed in CdX (X = S, Se, Te) NCs and their hybrids, but circularly polarized luminescence (CPL) in these fluorescent nanomaterials has been seldom reported. Herein, we describe the successful preparation of l- and d-cysteine-capped CdSe-dot/CdS-rods (DRs) with tunable CD and CPL behaviors and a maximum anisotropic factor ( g) of 4.66 × 10. The observed CD and CPL activities are sensitive to the relative absorption ratio of the CdS shell to the CdSe core, suggesting that the anisotropic g-factors in both CD and CPL increase to some extent for a smaller shell-to-core absorption ratio. In addition, the molar ratio of chiral cysteine to the DRs is investigated. Instead of enhancing the chiral interactions between the chiral molecules and DRs, an excess of cysteine molecules in aqueous solution inhibits both the CD and CPL activities. Such chiral and emissive NCs provide an ideal platform for the rational design of semiconductor nanomaterials with chiroptical properties.

Crystalline porous metal–organic frameworks (MOFs) with nanometer‐sized void spaces, large surface areas and ordered reticular motifs have offered a platform for achieving disruptive successes in divisional fields. Great progress in exploring the linear and nonlinear optical features of MOFs has been achieved, yet third‐order optical nonlinearities in two‐dimensional (2D) MOFs have rarely been studied. Here, a broadband nonlinear optical amplitude modification and phase shift are demonstrated in a few‐layer nickel‐p‐benzenedicarboxylic acid MOF (Ni‐MOF). The calculated bandgap of Ni‐MOF decreases from 3.12 eV to 0.85 eV as the doping of Ni ions increases, indicating the ability of this material to be used for optical amplitude modulation from the visible to the near‐infrared region, which is experimentally confirmed via a Z‐scan technique. The determined third‐order optical nonlinearities resemble those of other low‐dimensional nonlinear optical materials, suggesting the wide potential of Ni‐MOF for application in optoelectronics. As an example, a Ni‐MOF‐based saturable absorber was implemented into fiber resonators to demonstrate its broadband mode‐locking operations. A femtosecond laser pulse was readily obtained in the telecommunication wavelength window in an integrated all‐fiber resonator. Considering the chemical compatibility and rich variability, these primary investigations pave the way towards advanced photonics based on multifeature MOF materials.

The study aims to determine the efficacy and feasibility of a novel folate receptor (FR)-based circulating tumor cell (CTC) detection method in the diagnosis of non-small cell lung cancer (NSCLC). CTCs were collected from 3 ml of blood based on negative enrichment by immunomagnetic beads and then labeled by a conjugate of a tumor-specific ligand folate and an oligonucleotide. After washing off redundant conjugates, the bound conjugates were removed and analyzed by quantitative polymerase chain reaction. The captured cells were validated as tumor cells by immunofluorescence staining. In the evaluation of clinical utility, the results showed that the CTC levels of 153 patients with NSCLC were significantly higher than the controls (49 healthy donors and 64 patients with benign lung diseases; P < .001). With a threshold of 8.64 CTC units, the method showed a sensitivity of 73.2% and a specificity of 84.1% in the diagnosis of NSCLC, especially a sensitivity of 67.2% in stage I disease. Compared with the existing clinical biomarkers such as neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), cyfra21-1, and squamous cell carcinoma antigen (SCC Ag), the method showed the highest diagnostic efficiency (area under the curve, 0.823; 95% confidence interval, 0.773-0.874). Together, our results demonstrated that FR-positive CTCs were feasible diagnostic biomarkers in patients with NSCLC, as well as in early-stage tumors.

We constructed an intelligent cloud lab that integrates lab automation with cloud servers and artificial intelligence (AI) to detect chirality in perovskites. Driven by the materials acceleration operating system in cloud (MAOSIC) platform, on-demand experimental design by remote users was enabled in this cloud lab. By employing artificial intelligence of things (AIoT) technology, synthesis, characterization, and parameter optimization can be autonomously achieved. Through the remote collaboration of researchers, optically active inorganic perovskite nanocrystals (IPNCs) were first synthesized with temperature-dependent circular dichroism (CD) and inversion control. The inter-structure (structural patterns) and intrastructure (screw dislocations) dual-pattern-induced mechanisms detected by MAOSIC were comprehensively investigated, and offline theoretical analysis revealed the thermodynamic mechanism inside the materials. This self-driving cloud lab enables efficient and reliable collaborations across the world, reduces the setup costs of in-house facilities, combines offline theoretic analysis, and is practical for accelerating the speed of material discovery.

Black-phosphorus-analogue tin monosulfide was successfully introduced to resolve the instability issue of black phosphorus.

Background: It is critical to develop a non-invasive and accurate method for differentiating between malignant and benign solitary pulmonary nodules. In large sample studies, the effectiveness of the diagnostic prediction model as a tool of assessment of the probability of malignancy is still unclear. The establishment of a diagnostic model based on large samples is needed. Methods: In this study, 3358 patients diagnosed with a solitary pulmonary nodule between January 2005 and March 2013, were enrolled. All patients received surgery for pulmonary nodule resection. Clinical characters, preoperative biomarker results, and computed tomography scan findings were collected. All patients were randomly separated into a training set (n = 1679) and a test set (n = 1679); we used training sets to build a diagnostic model for the malignancy probability of pulmonary nodules, and applied the test set to validate our model, as well as other published diagnostic models. Result: Logistic regression analysis identified 11 clinical characteristics as independent predictors of malignancy in patients with a solitary pulmonary nodule. The goodness-of-fit statistic for the model indicated that the observed proportion of malignancies did not differ from the predicted proportion (P = 0.571). The area under the curves of the receiver operator characteristic curve for our model in the training set was 0.935. Conclusion: As the accuracy of the model was high, we suggest that the diagnostic model can be used as a tool to help guiding clinical decisions, when the clinician cannot make a definitive diagnosis of a solitary pulmonary nodule.

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