The electrochemical N2 fixation, which is far from practical application in aqueous solution under ambient conditions, is extremely challenging and requires a rational design of electrocatalytic ...centers. We observed that bismuth (Bi) might be a promising candidate for this task because of its weak binding with H adatoms, which increases the selectivity and production rate. Furthermore, we successfully synthesized defect‐rich Bi nanoplates as an efficient noble‐metal‐free N2 reduction electrocatalyst via a low‐temperature plasma bombardment approach. When exclusively using 1H NMR measurements with N2 gas as a quantitative testing method, the defect‐rich Bi(110) nanoplates achieved a 15NH3 production rate of 5.453 μg mgBi−1 h−1 and a Faradaic efficiency of 11.68 % at −0.6 V vs. RHE in aqueous solution at ambient conditions.
Beneficial defects: Defect‐rich bismuth nanoplates achieve a 15NH3 production rate of 5.453 μg mgBi−1 h−1 and a Faradaic efficiency of 11.68 % at −0.6 V vs. RHE in aqueous solutions at ambient conditions because of their poor binding with H adatoms, which increases the selectivity and production rate. Also, 1H NMR measurements with N2 gas ware used as a quantitative test method in aqueous electrolytes.
Objectives
This study aimed to establish a machine learning prediction model that can be used to predict bone metastasis (BM) in patients with newly diagnosed thyroid cancer (TC).
Methods
Demographic ...and clinicopathologic variables of TC patients in the Surveillance, Epidemiology, and End Results database from 2010 to 2016 were retrospectively analyzed. On this basis, we developed a random forest (RF) algorithm model based on machine‐learning. The area under receiver operating characteristic curve (AUC), accuracy score, recall rate, and specificity are used to evaluate and compare the prediction performance of the RF model and the other model.
Results
A total of 17,138 patients were included in the study, with 166 (0.97%) developed bone metastases. Grade, T stage, histology, race, sex, age, and N stage were the important prediction features of BM. The RF model has better predictive performance than the other model (AUC: 0.917, accuracy: 0.904, recall rate: 0.833, and specificity: 0.905).
Conclusions
The RF model constructed in this study could accurately predict bone metastases in TC patients, which may provide clinicians with more personalized clinical decision‐making recommendations. Machine learning technology has the potential to improve the development of BM prediction models in TC patients.
We developed a random forest prediction model for bone metastases in thyroid cancer (TC) patients. This facilitates personalized diagnosis and refined clinical decision making for bone metastases in TC patients.
Nucleophilic substitutions are fundamentally important transformations in synthetic organic chemistry. Despite the substantial advances in bimolecular nucleophilic substitutions (SN2) at saturated ...carbon centers, analogous SN2 reaction at the amide nitrogen atom remains extremely limited. Here we report an SN2 substitution method at the amide nitrogen atom with amine nucleophiles for nitrogen–nitrogen (N−N) bond formation that leads to a novel strategy toward biologically and medicinally important hydrazide derivatives. We found the use of sulfonate‐leaving groups at the amide nitrogen atom played a pivotal role in the reaction. This new N−N coupling reaction allows the use of O‐tosyl hydroxamates as electrophiles and readily available amines, including acyclic aliphatic amines and saturated N‐heterocycles as nucleophiles. The reaction features mild conditions, broad substrate scope (>80 examples), excellent functional group tolerability, and scalability. The method is applicable to late‐stage modification of various approved drug molecules, thus enabling complex hydrazide scaffold synthesis.
A new N−N coupling approach to the practical synthesis of hydrazides is disclosed by employing an SN2 strategy at electrophilic amides with nucleophilic amines. The reaction exhibits mild conditions, broad substrate scope, excellent functional group tolerance, easy scalability, and is applicable to the late‐stage modification of various approved drug molecules, thus enabling complex hydrazide scaffold synthesis.
Objectives
To evaluate the prediction performance of deep convolutional neural network (DCNN) based on ultrasound (US) images for the assessment of breast cancer molecular subtypes.
Methods
A dataset ...of 4828 US images from 1275 patients with primary breast cancer were used as the training samples. DCNN models were constructed primarily to predict the four St. Gallen molecular subtypes and secondarily to identify luminal disease from non-luminal disease based on the ground truth from immunohistochemical of whole tumor surgical specimen. US images from two other institutions were retained as independent test sets to validate the system. The models’ performance was analyzed using per-class accuracy, positive predictive value (PPV), and Matthews correlation coefficient (MCC).
Results
The model achieved good performance in identifying the four breast cancer molecular subtypes in the two test sets, with accuracy ranging from 80.07% (95% CI, 76.49–83.23%) to 97.02% (95% CI, 95.22–98.16%) and 87.94% (95% CI, 85.08–90.31%) to 98.83% (95% CI, 97.60–99.43) for the two test cohorts for each sub-category, respectively. In terms of 4-class weighted average MCC, the model achieved 0.59 for test cohort A and 0.79 for test cohort B. Specifically, the DCNN also yielded good diagnostic performance in discriminating luminal disease from non-luminal disease, with a PPV of 93.29% (95% CI, 90.63–95.23%) and 88.21% (95% CI, 85.12–90.73%) for the two test cohorts, respectively.
Conclusion
Using pretreatment US images of the breast cancer, deep learning model enables the assessment of molecular subtypes with high diagnostic accuracy.
Trial registration
Clinical trial number: ChiCTR1900027676
Key Points
• Deep convolutional neural network (DCNN) helps clinicians assess tumor features with accuracy.
• Multicenter retrospective study shows that DCNN derived from pretreatment ultrasound imagine improves the prediction of breast cancer molecular subtypes.
• Management of patients becomes more precise based on the DCNN model.
Alkyl boronic acid and its derivatives are important motifs in organic synthesis and pharmaceuticals. Herein, we report a photoinduced, CuCl
2
mediated C(sp
3
)-H borylation of unactivated alkanes. ...This protocol features mild reaction conditions, readily available reagents, and gram-scalability.
This paper describes a photoinduced, CuCl
2
-mediated C(sp
3
)-H borylation of non-activated alkanes. The new reaction features mild conditions, readily available reagents, and easy scalability.
The aim of the study was to develop and validate a deep learning radiomic nomogram (DLRN) for preoperatively assessing breast cancer pathological complete response (pCR) after neoadjuvant ...chemotherapy (NAC) based on the pre- and post-treatment ultrasound.
Patients with locally advanced breast cancer (LABC) proved by biopsy who proceeded to undergo preoperative NAC were enrolled from hospital #1 (training cohort, 356 cases) and hospital #2 (independent external validation cohort, 236 cases). Deep learning and handcrafted radiomic features reflecting the phenotypes of the pre-treatment (radiomic signature RS 1) and post-treatment tumour (RS2) were extracted. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used for feature selection and RS construction. A DLRN was then developed based on the RSs and independent clinicopathological risk factors. The performance of the model was assessed with regard to calibration, discrimination and clinical usefulness.
The DLRN predicted the pCR status with accuracy, yielded an area under the receiver operator characteristic curve of 0.94 (95% confidence interval, 0.91–0.97) in the validation cohort, with good calibration. The DLRN outperformed the clinical model and single RS within both cohorts (P < 0.05, as per the DeLong test) and performed better than two experts' prediction of pCR (both P < 0.01 for comparison of total accuracy). Besides, prediction within the hormone receptor–positive/human epidermal growth factor receptor 2 (HER2)–negative, HER2+ and triple-negative subgroups also achieved good discrimination performance, with an AUC of 0.90, 0.95 and 0.93, respectively, in the external validation cohort. Decision curve analysis confirmed that the model was clinically useful.
A deep learning–based radiomic nomogram had good predictive value for pCR in LABC, which could provide valuable information for individual treatment.
•A novel preoperative pathological complete response prediction model was developed.•The model is based on pre- and post-neoadjuvant chemotherapy ultrasound images.•It yielded an area under the receiver operator characteristic curve AUC of 0.94 in the independent external validation cohort.•It outperformed two experts who evaluated the pathological complete response status.•It may facilitate tailoring the optimum extent of breast and axillary surgery.
Background
Diagnostic criteria for sarcopenia have not been established in Chinese. This study established criteria based on the L3‐skeletal muscle index (L3‐SMI) and assessed its value for outcomes ...predicting in cirrhotic Chinese patients.
Methods
Totally 911 subjects who underwent a CT scan at two centres were enrolled in Cohort 1 (394 male and 417 female subjects, aged 20–80 years). The data of those subjects younger than 60 years (365 male and 296 female subjects) were used to determine the reference intervals of the L3‐SMI and its influencing factors. Cohort 2 consisted of 480 patients (286 male and 184 female patients) from three centres, and their data were used to investigate the prevalence of sarcopenia and evaluate the value of L3‐SMI for predicting the prognosis and complications of cirrhosis.
Results
Age and sex had the greatest effects on the L3‐SMI (P < 0.001). The L3‐SMI scores were clearly higher in male patients than in female patients (52.94 ± 8.41 vs. 38.91 ± 5.65 cm2/m2, P < 0.001) and sharply declined in subjects aged ≥ 60 years. Based on the mean −1.28 × SD among adults aged < 60 years, the L3‐SMI cut‐off value for sarcopenia was 44.77 cm2/m2 in male patients and 32.50 cm2/m2 in female patients. Using these values, 22.5% of the cirrhotic patients (28.7% of male patients and 11.9% of female patients) were diagnosed with sarcopenia. Compared with non‐sarcopenia individuals, sarcopenia patients had lower body mass index (21.28 ± 3.01 vs. 24.09 ± 3.39 kg/m2, P < 0.001) and serum albumin levels (31.54 ± 5.93 vs. 32.93 ± 5.95 g/L, P = 0.032), longer prothrombin times (16.39 ± 3.05 vs. 15.71 ± 3.20 s, P = 0.049), higher total bilirubin concentrations (41.33 ± 57.38 vs. 32.52 ± 31.48 μmol/L, P = 0.039), worse liver function (Child–Pugh score, 8.05 ± 2.11 vs. 7.32 ± 2.05, P = 0.001), higher prevalence of cirrhosis‐related complications (81.82% vs. 62.24%, P < 0.001) and mortality (30.68% vs. 11.22%, P < 0.001). Overall survival was significantly lower in the sarcopenia group risk ratio (RR) = 2.643, 95% confidence interval (CI) 1.646–4.244, P < 0.001, accompanied with an increased cumulative incidence of ascites (RR = 1.827, 95% CI 1.259–2.651, P = 0.002), spontaneous bacterial peritonitis (RR = 3.331, 95% CI 1.404–7.903, P = 0.006), hepatic encephalopathy (RR = 1.962, 95% CI 1.070–3.600, P = 0.029), and upper gastrointestinal varices (RR = 2.138, 95% CI 1.319–3.466, P = 0.002). Subgroup analysis showed sarcopenia shortened the survival of the patients with Model For End‐Stage Liver Disease score > 14 (RR = 4.310, 95% CI 2.091–8.882, P < 0.001) or Child–Pugh C (RR = 3.081, 95% CI 1.516–6.260, P = 0.002).
Conclusions
Sarcopenia is a common comorbidity of cirrhosis and can be used to predict cirrhosis‐related complications and the prognosis.
Constituting approximately 10% of flowering plant species, orchids (Orchidaceae) display unique flower morphologies, possess an extraordinary diversity in lifestyle, and have successfully colonized ...almost every habitat on Earth. Here we report the draft genome sequence of Apostasia shenzhenica, a representative of one of two genera that form a sister lineage to the rest of the Orchidaceae, providing a reference for inferring the genome content and structure of the most recent common ancestor of all extant orchids and improving our understanding of their origins and evolution. In addition, we present transcriptome data for representatives of Vanilloideae, Cypripedioideae and Orchidoideae, and novel third-generation genome data for two species of Epidendroideae, covering all five orchid subfamilies. A. shenzhenica shows clear evidence of a whole-genome duplication, which is shared by all orchids and occurred shortly before their divergence. Comparisons between A. shenzhenica and other orchids and angiosperms also permitted the reconstruction of an ancestral orchid gene toolkit. We identify new gene families, gene family expansions and contractions, and changes within MADS-box gene classes, which control a diverse suite of developmental processes, during orchid evolution. This study sheds new light on the genetic mechanisms underpinning key orchid innovations, including the development of the labellum and gynostemium, pollinia, and seeds without endosperm, as well as the evolution of epiphytism; reveals relationships between the Orchidaceae subfamilies; and helps clarify the evolutionary history of orchids within the angiosperms.
Objectives
To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer.
Methods
Data of 303 ...patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status–related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration.
Results
SWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (overall C-index: 0.842; 95%CI, 0.773–0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765–0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥ 1)), it achieved a C-index of 0.845 (95%CI, 0.777–0.914) for the training cohort and 0.817 (95%CI, 0.769–0.865) for the validation cohort. The tool could also discriminate between low (N + (1–2)) and heavy metastatic ALN burden (N + (≥ 3)), with a C-index of 0.827 (95%CI, 0.742–0.913) in the training cohort and 0.810 (95%CI, 0.755–0.864) in the validation cohort.
Conclusion
The radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making.
Key Points
•
Radiomics analysis helps radiologists to evaluate the axillary lymph node status of breast cancer with accuracy.
•
This multicentre retrospective study showed that radiomics nomogram based on shear
-
wave elastography provides incremental information for risk stratification.
•
Treatment can be given with more precision based on the model.
Herein, we present a stable water‐soluble cobalt complex supported by a dianionic 2,2′‐(2,2′‐bipyridine‐6,6′‐diyl)bis(propan‐2‐ol) ligand scaffold, which is a rare example of a high‐oxidation ...species, as demonstrated by structural, spectroscopic and theoretical data. Electron paramagnetic resonance (EPR) spectroscopy and magnetic susceptibility measurements revealed that the CoIV center of the mononuclear complex in the solid state resides in the high spin state (sextet, S=5/2). The complex can effectively catalyze water oxidation via a single‐site water nucleophilic attack pathway with an overpotential of only 360 mV in a phosphate buffer with a pH of 6. The key intermediate toward water oxidation was speculated based on theoretical calculations and was identified by in situ spectroelectrochemical experiments. The results are important regarding the accessibility of high‐oxidation state metal species in synthetic models for achieving robust and reactive oxidation catalysis.
A stable water‐soluble cobalt(IV) complex supported by a dianionic 2,2′‐(2,2′‐bipyridine‐6,6′‐diyl)bis(propan‐2‐ol) ligand scaffold is very active in the catalysis of water oxidation at an overpotential of only 360 mV at pH=6.
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