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Design and optimization of a negative pressure rotary cutting safflower filaments harvesting device
Key process in sex-cell formation can finally be studied in vitro
Elevated troponin levels in rhabdomyolysis as a predictor of mortality in patients with normal kidney and cardiac function
The association of life’s essential 8 scores trajectory patterns with the risk of all cancer types
How the NIH dominates the world’s health research — in charts
An opponent striatal circuit for distributional reinforcement learning
Experimental wavefront sensing techniques based on deep learning models using a Hartmann-Shack sensor for visual optics applications
Distributional outcomes of urban heat island reduction pathways under climate extremes
Serum metabolomics indicates ferroptosis in patients with pantothenate kinase associated neurodegeneration
Abstract The core syndrome among NBIA disorders is pantothenate kinase-associated neurodegeneration (PKAN), an autosomal recessive disorder caused by mutations in the PANK2 gene. There is no therapy for PKAN; only symptomatic treatment is available. Our work aimed to identify the mechanisms induced by biochemical disturbances in the cell cycle and identify potential pharmacological targets to improve patient quality of life. Mass spectrometry (MS) (metals) and NMR spectroscopy (hydrophilic and hydrophobic compounds) were used for profile analyses of the sera of 12 PKAN patients and 12 controls to study the compounds involved in PKAN pathomechanisms. We performed ANOVA and multivariate analysis using orthogonal partial least squares discriminant analysis. We have shown for the first time that patients have 100–500-fold greater serum citrate levels than controls do, which may contribute to Fe transport and ferroptosis. Ferroptosis may be indicated by disturbances in the levels of many metals, oxidative stress, disturbances in energy production and neurotransmission or dysfunction of biological membranes. Our findings suggest that ferroptosis could be a primary cause of cell death in PKAN patients. This could be indicated by serum metabolomics.
‘Scientists will not be silenced’: thousands protest Trump research cuts
Study of the vibration frequency characteristics of tectonic coal containing assemblages at different loading rates
Microsoft quantum computing ‘breakthrough’ faces fresh challenge
Human-correlated genetic models identify precision therapy for liver cancer
Abstract Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, is a leading cause of cancer-related mortality worldwide 1,2 . HCC occurs typically from a background of chronic liver disease, caused by a spectrum of predisposing conditions. Tumour development is driven by the expansion of clones that accumulate progressive driver mutations 3 , with hepatocytes the most likely cell of origin 2 . However, the landscape of driver mutations in HCC is broadly independent of the underlying aetiologies 4 . Despite an increasing range of systemic treatment options for advanced HCC, outcomes remain heterogeneous and typically poor. Emerging data suggest that drug efficacies depend on disease aetiology and genetic alterations 5,6 . Exploring subtypes in preclinical models with human relevance will therefore be essential to advance precision medicine in HCC 7 . Here we generated a suite of genetically driven immunocompetent in vivo and matched in vitro HCC models. Our models represent multiple features of human HCC, including clonal origin, histopathological appearance and metastasis. We integrated transcriptomic data from the mouse models with human HCC data and identified four common human–mouse subtype clusters. The subtype clusters had distinct transcriptomic characteristics that aligned with the human histopathology. In a proof-of-principle analysis, we verified response to standard-of-care treatment and used a linked in vitro–in vivo pipeline to identify a promising therapeutic candidate, cladribine, that has not previously been linked to HCC treatment. Cladribine acts in a highly effective subtype-specific manner in combination with standard-of-care therapy.
CD206 and dust particles are prognostic biomarkers of progressive fibrosing interstitial lung disease associated with air pollutant exposure
Machine learning enabled dual to wideband frequency agile $$\:{\varvec{A}\varvec{l}}_{2}{\varvec{O}}_{3}\:$$ceramic-based dielectric MIMO antenna for 5G new radio applications
Abstract This article presents a dual-band to wideband Frequency Agile (FA) rectangular dielectric resonator (DR) based hybrid MIMO antenna for 5G New Radio (NR) application with connected ground. The DR is made of Al2O3 (εr = 9.8) ceramic material. The FA is achieved through the PIN Diode switches. When the PIN Diode is in an “ON” state, it provides dual bands due to the excitation of TE 111 mode. When the PIN Diode is in an “OFF” state, it provides wideband characteristics due to the excitation of TE 111 and TE 211 modes in the rectangular DR. The isolation and gain are achieved by 20 dB and 4.3 dBi, respectively. The maximum tuning range is 49.36. The MIMO performance characteristics are achieved within the allowable range. A good agreement is achieved between the simulated and measured results. The suggested MIMO antenna is optimized through the various ML algorithms in which Random Forest (RF) ML algorithms achieved the highest accuracy more than 99% compared to other ML algorithms for S-parameters prediction. Hence, it is suitable for 5G NR applications.