PDE5 Overexpression in Well-Differentiated Thyroid Carcinomas Is Associated with Lymph Node Metastasis

Overexpression of PDE5 is observed in certain human cancers, but PDE5 expression in well-differentiated thyroid carcinoma (WDTC) is unknown. We therefore examined PDE5 expression and its relationship with the clinicopathological features of WDTC. Real-time qPCR and Western blotting were performed to analyze the expression of PDE5 mRNA and protein in paired WDTC tumor and adjacent nontumor tissues. Immunohistochemistry was used to analyze the expression of PDE5 in paraffin-embedded tissues obtained from 103 cases of WDTC. Statistical analyses were performed to examine the correlation between PDE5 expression and clinicopathological features. The expression of PDE5 mRNA and protein was upregulated in WDTC lesions compared to their paired noncancerous tissues. The expression of PDE5 was significantly correlated with age (P=0.032), regional lymph node status (P=0.004), and the presence of distant metastasis (P=0.020). High PDE5 expression was more closely associated with lymph node involvement in patients over 45 years (OR = 15.60, P≤0.05). Thus, PDE5 may be a potential biomarker in WDTC, particularly in patients with regional lymph node metastasis, which is associated with disease recurrence, treatment failure, and morbidity. PDE5 expression may also help predict the prognosis and recurrence of WDTC after surgery

Efficacy of guideline-directed medical treatment in heart failure with mildly reduced ejection fraction.

Heart failure with mildly reduced ejection fraction (HFmrEF) has received increasing attention following the publication of the latest ESC guidelines in 2021. However, it remains unclear whether patients with HFmrEF could benefit from guideline-directed medical treatment (GDMT), referring the combination of ACEI/ARB/ARNI, β-blockers, and MRAs, which are recommended for those with reduced ejection fraction. This study explored the efficacy of GDMT in HFmrEF patients. This was a retrospective cohort study of HFmrEF patients admitted to The First Affiliated Hospital of Dalian Medical University between 1 September 2015 and 30 November 2019. Propensity score matching (1:2) between patients receiving triple-drug therapy (TT) and non-triple therapy (NTT) based on age and sex was performed. The primary outcome was all cause death, cardiac death, rehospitalization from any cause, and rehospitalization due to worsening heart failure. Of the 906 patients enrolled in the matched cohort (TT group, n = 302; NTT group, N = 604), 653 (72.08%) were male, and mean age was 61.1 ± 11.92. Survival analysis suggested that TT group experienced a significantly lower incidence of prespecified primary endpoints than NTT group. Multivariable Cox regression showed that TT group had a lower risk of all-cause mortality (HR 0.656, 95% CI 0.447-0.961, P = 0.030), cardiac death (HR 0.599, 95% CI 0.380-0.946, P = 0.028), any-cause rehospitalization (HR 0.687, 95% CI 0.541-0.872, P = 0.002), and heart failure rehospitalization (HR 0.732, 95% CI 0.565-0.948, P = 0.018). In patients with HFmrEF, combined use of neurohormonal antagonists produces remarkable effects in reducing the occurrence of the primary outcome of rehospitalization and death. Thus, the treatment of HFmrEF should be categorized as HFrEF due to the similar benefit of neurohormonal blocking therapy in HFrEF and HFmrEF. [Abstract copyright: © 2022 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.

Pruning random resistive memory for optimizing analogue AI

The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and environmental sustainability. One promising solution is to revisit analogue computing, a technique that predates digital computing and exploits emerging analogue electronic devices, such as resistive memory, which features in-memory computing, high scalability, and nonvolatility. However, analogue computing still faces the same challenges as before: programming nonidealities and expensive programming due to the underlying devices physics. Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network. Software-wise, the topology of a randomly weighted neural network is optimized by pruning connections rather than precisely tuning resistive memory weights. Hardware-wise, we reveal the physical origin of the programming stochasticity using transmission electron microscopy, which is leveraged for large-scale and low-cost implementation of an overparameterized random neural network containing high-performance sub-networks. We implemented the co-design on a 40nm 256K resistive memory macro, observing 17.3% and 19.9% accuracy improvements in image and audio classification on FashionMNIST and Spoken digits datasets, as well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE datasets, respectively. This is accompanied by 82.1%, 51.2%, and 99.8% improvement in energy efficiency thanks to analogue in-memory computing. By embracing the intrinsic stochasticity and in-memory computing, this work may solve the biggest obstacle of analogue computing systems and thus unleash their immense potential for next-generation AI hardware

Mass spectrometry-based metabolomics for discovering active ingredients and exploring action mechanism of herbal medicine

Natural products derived from herbal medicine are a fruitful source of lead compounds because of their structural diversity and potent bioactivities. However, despite the success of active compounds derived from herbal medicine in drug discovery, some approaches cannot effectively elucidate the overall effect and action mechanism due to their multi-component complexity. Fortunately, mass spectrometry-based metabolomics has been recognized as an effective strategy for revealing the effect and discovering active components, detailed molecular mechanisms, and multiple targets of natural products. Rapid identification of lead compounds and isolation of active components from natural products would facilitate new drug development. In this context, mass spectrometry-based metabolomics has established an integrated pharmacology framework for the discovery of bioactivity-correlated constituents, target identification, and the action mechanism of herbal medicine and natural products. High-throughput functional metabolomics techniques could be used to identify natural product structure, biological activity, efficacy mechanisms, and their mode of action on biological processes, assisting bioactive lead discovery, quality control, and accelerating discovery of novel drugs. These techniques are increasingly being developed in the era of big data and use scientific language to clarify the detailed action mechanism of herbal medicine. In this paper, the analytical characteristics and application fields of several commonly used mass spectrometers are introduced, and the application of mass spectrometry in the metabolomics of traditional Chinese medicines in recent years and its active components as well as mechanism of action are also discussed

Efficacy of guideline‐directed medical treatment in heart failure with mildly reduced ejection fraction

Aims: Heart failure with mildly reduced ejection fraction (HFmrEF) has received increasing attention following the publication of the latest ESC guidelines in 2021. However, it remains unclear whether patients with HFmrEF could benefit from guideline‐directed medical treatment (GDMT), referring the combination of ACEI/ARB/ARNI, β‐blockers, and MRAs, which are recommended for those with reduced ejection fraction. This study explored the efficacy of GDMT in HFmrEF patients. Methods: This was a retrospective cohort study of HFmrEF patients admitted to The First Affiliated Hospital of Dalian Medical University between 1 September 2015 and 30 November 2019. Propensity score matching (1:2) between patients receiving triple‐drug therapy (TT) and non‐triple therapy (NTT) based on age and sex was performed. The primary outcome was all cause death, cardiac death, rehospitalization from any cause, and rehospitalization due to worsening heart failure. Results: Of the 906 patients enrolled in the matched cohort (TT group, n = 302; NTT group, N = 604), 653 (72.08%) were male, and mean age was 61.1 ± 11.92. Survival analysis suggested that TT group experienced a significantly lower incidence of prespecified primary endpoints than NTT group. Multivariable Cox regression showed that TT group had a lower risk of all‐cause mortality (HR 0.656, 95% CI 0.447–0.961, P = 0.030), cardiac death (HR 0.599, 95% CI 0.380–0.946, P = 0.028), any‐cause rehospitalization (HR 0.687, 95% CI 0.541–0.872, P = 0.002), and heart failure rehospitalization (HR 0.732, 95% CI 0.565–0.948, P = 0.018). Conclusions: In patients with HFmrEF, combined use of neurohormonal antagonists produces remarkable effects in reducing the occurrence of the primary outcome of rehospitalization and death. Thus, the treatment of HFmrEF should be categorized as HFrEF due to the similar benefit of neurohormonal blocking therapy in HFrEF and HFmrEF

Readily recyclable carbon fiber reinforced composites based on degradable thermosets: a review

Readily recyclable carbon fiber reinforced composites based on degradable thermosets: a revie

Robust, Fire-Safe, Monomer-Recovery, Highly Malleable Thermosets from Renewable Bioresources

Conventional thermosets are built by nonrenewable fossil resources and are arduous to be reprocessed, recycled, and reshaped due to their permanent covalent cross-linking, and their flammability makes them unsafe during use. Here, for the first time, we synthesized a novel Schiff base precursor from abundant and renewable lignin derivative vanillin and produced malleable thermosets (Schiff base covalent adaptable networks (CANs)) combining high performance, super-rapid reprocessability, excellent monomer recovery, and arbitrary permanent shape changeability as well as outstanding fire resistance. The Schiff base CANs exhibited high glass transition temperatures of similar to 178 degrees C, tensile strength of similar to 69 MPa, tensile modulus of similar to 1925 MPa, excellent flame retardancy with UL-94 V0 rating and V1 rating, and high LOI of similar to 30%. Meanwhile, three Schiff base CANs showed high malleability with the activation energy of the bond exchange of 49-81 kJ mol(-1) and could be reprocessed in 2-10 min at 180 degrees C. These Schiff base CANs provide a prime example to foster the development of advanced thermosetting materials from renewable bioresources

Robust, Fire-Safe, Monomer-Recovery, Highly Malleable Thermosets from Renewable Bioresources

Conventional thermosets are built by nonrenewable fossil resources and are arduous to be reprocessed, recycled, and reshaped due to their permanent covalent cross-linking, and their flammability makes them unsafe during use. Here, for the first time, we synthesized a novel Schiff base precursor from abundant and renewable lignin derivative vanillin and produced malleable thermosets (Schiff base covalent adaptable networks (CANs)) combining high performance, super-rapid reprocessability, excellent monomer recovery, and arbitrary permanent shape changeability as well as outstanding fire resistance. The Schiff base CANs exhibited high glass transition temperatures of similar to 178 degrees C, tensile strength of similar to 69 MPa, tensile modulus of similar to 1925 MPa, excellent flame retardancy with UL-94 V0 rating and V1 rating, and high LOI of similar to 30%. Meanwhile, three Schiff base CANs showed high malleability with the activation energy of the bond exchange of 49-81 kJ mol(-1) and could be reprocessed in 2-10 min at 180 degrees C. These Schiff base CANs provide a prime example to foster the development of advanced thermosetting materials from renewable bioresources

Solar Photovoltaic Investment Changes across China Regions Using a Spatial Shift-Share Analysis

Solar photovoltaic (PV) has become the fastest-growing new energy in China and one of the main contributors to China’s clean energy transition. From 2013 to 2019, China’s solar PV installed capacity grew from 15,890 MW to 204,180 MW, increasing by 11.85 times. To explore solar PV investment changes across China regions, we use spatial shift-share analysis model to decompose solar PV investment changes from 2013 to 2019 into four components: national energy investment growth effect (NEG), national energy investment structure effect (NES), neighbor–nation solar PV investment competitive effect (NNC), and region–neighbor solar PV investment competitive effect (RNC). Based on the decomposition results, we find that the value of NNC of most western provinces is negative for the entire period, while the NNC of most central and eastern provinces is in the middle and lower range. There is little difference in RNC among these regions. While comparing the influence caused by the four effects, NNC and RNC play dominant roles in solar PV investment changes in eastern and central provinces, which means NEG and NES have relatively small impacts. By contrast, NEG and NES affect the solar PV investment changes at a larger scale in most western provinces. Comparing the NNC and RNC, we find that RNC played a prominent role in the eastern and central regions, while NNC played a dominant role in the west