Target-modulated mineralization of wood channels as enzyme-free electrochemical sensors for detecting amyloid-ß species.

Alzheimer's disease (AD) is an irreversible brain disorder, which has been found to be associated with neurotoxic amyloid-ß oligomers (AßO). The early diagnosis of AD is still a great challenge. Herein, inspired by the hierarchical channel structure of natural wood, we design and demonstrate a low-cost and sensitive wood channel-based fluidic membrane for electrochemical sensing of AßO1-42. In this design, Zn/Cu-2-methylimidazole (Zn/Cu-Hmim) with artificial peroxidase (POD)-like activity was asymmetrically fabricated at one side of the wood channels by biomimetic mineralization and a subsequent ion exchange reaction. The strong affinity between Cu(II) and AßO1-42 enables Cu(II) species in Zn/Cu-Hmim to be extracted by AßO1-42, thus suppressing the POD-like performance via Zn/Cu-Hmim disassembly. Using Zn/Cu-Hmim to catalyze the oxidation reaction of 2,2'-diazo-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) by H2O2, the current-voltage (I-V) properties of wood channels are influenced by the generated oxidation product (ABTS•+), thus providing information useful for the quantitative analysis of AßO1-42. Importantly, the three aggregation states of Aß1-42 (AßM1-42, AßO1-42, and AßF1-42) can also be identified, owing to the affinity difference and available reaction sites. The proposed wood membrane provides a novel, assessable, and scalable channel device to develop sensitive electrochemical sensors; moreover, the sustainable wood materials represent alternative candidates for developing channel-structured sensing platforms.

From growth to sustainability: investigating N-shaped EKC and the role of energy productivity, technological advancement, and human capital in OECD economies.

Environmental degradation rates have been on a concerning upward trajectory in recent decades, directly threatening the well-being of global populations. Responding to this urgent matter, scholars have been driven to explore its nuances, particularly emphasizing lowering energy consumption and carbon emissions amidst the growing demands of growing economies. Achieving the targets outlined in the 2015 Paris Climate Agreement has also become a priority for many countries. Therefore, this study scrutinizes the Environmental Kuznets Curve (EKC) hypothesis, specifically focusing on the role of energy productivity, technological advancement, and human capital in fostering a sustainable environment across 35 OECD economies from 1990 to 2018. Utilizing three robust econometric techniques, Cross-Sectional Autoregressive Distributed Lag (CS-ARDL), Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS), we have drawn insightful conclusions from our data. The analysis substantiates an N-shaped EKC hypothesis relationship between GDP and CO2 emissions, pointing towards an initially increasing, then decreasing, and finally an increasing again trend of emissions with GDP. Furthermore, the long-term projections underscore that energy productivity, technological progression, and human capital formation harm the environment. These findings culminate in a call for governments to orchestrate extensive plans and initiatives. This involves promoting green technologies, renewable energy-based ideas, and comprehensive education and awareness programs. These efforts should span all educational levels, highlighting climate change, sustainable practices, and the need for CO2 reduction, empowering societies to contribute to a sustainable future.

Enantioselective Target Transport-Mediated Nanozyme Decomposition for the Identification of Reducing Enantiomers in Asymmetric Nanochannel Arrays.

Enantioselective identification of chiral molecules is regarded as one of the key issues in biological and medical sciences because of their configuration-dependent effects on biological systems. In this study, we developed an electrochemical platform based on a tandem recognition-reaction zone design in TiO2 nanochannels for the specific recognition of reducing enantiomers. In this system, MIL-125(Ti) Ti-metal-organic frameworks, in situ grown in TiO2 nanochannels, provided a homochiral recognition environment via postmodification with l-tartaric acid (l-TA); MnO2 nanosheets possessing both glucose oxidase (GOD)- and peroxidase (POD)-mimicking activities served as the target-reactive zone at the end of the nanochannels. The use of penicillamine (Pen) enantiomers as model-reducing targets facilitated the passage of d-Pen through the homochiral recognition zone, owing to its lower affinity with l-TA. The passed Pen molecules reached the responsive zone and induced a target concentration-dependent MnO2 disassembly. Such target recognition event impaired the cascade GOD- and POD-like activities of MnO2. Combining the enantioselectivity of the recognition nanochannels with the cascade enzyme-like activity of MnO2 toward glucose and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonate), the quantitative identification of l- and d-Pen was achieved through the changes in transmembrane ionic current induced by the generated charged products. This recognition-reaction zone design paves an effective way for developing a promising electrochemical platform for the identification of reducing enantiomers with improved selectivity and sensitivity.

Metal organic frameworks-in-nanochannels: A tailorable chromatography membrane for isolation of target protein.

Metal-organic frameworks (MOFs) demonstrate strong potential in biosample separation. However, the obtained MOFs powders are unsuitable for recovery techniques in an aqueous solution, especially the challenges of withdrawing MOFs particles and expanding their functions for specific applications. Herein, a general strategy is designed utilizing metal oxide-nanochannel arrays as precursors and templates for in-situ selective growth of MOFs structures. The exemplary MOFs (Ni-bipy) with tailored composition are selectively grown in NiO/TiO2 nanochannel membrane (NM) using NiO as the sacrificial precursor, which enables one to achieve a ∼262 times concentration of histidine-tagged proteins within 100 min. The significantly improved adsorption efficiency in a wide pH range and the effective enrichment from a complex matrix as a nanofilter illustrate the great potential of MOFs in nanochannels membranes for the high-efficiency recovery of essential proteins in complex biological samples. The porous self-aligned Ni-MOFs/TiO2 NM exhibits biocompatibility and flexible functionalities, which is desirable for the generation of multifunctional nanofilter devices and developing biomacromolecule delivery vehicles.

Target Recognition-Triggered Peroxidase-Mimicking Activity Depression in Homochiral Nanochannels for Identifying Cystine Enantiomers.

Enantioselective identification of chiral molecules is of paramount importance in medical science, biochemistry, and pharmaceutics owing to the configuration-dependent activities of enantiomers. However, the identical physicochemical properties of enantiomers remain challenging in chiral sensing. In this study, inspired by the peroxidase-mimicking activity of Fe(III)-based nanomaterials, an enantioselective artificial architecture is constructed on TiO2 nanochannels. Homochiral Ti-based metal-organic frameworks (MOFs) use a 2,2'-bipyridine-5,5'-dicarboxylic acid ligand as the artificial enzyme skeleton, Fe(III) as peroxidase-mimicking centers, and l-tartaric acid (TA) as a chiral recognition selector. Using l-/d-cystine as model enantiomers, the chiral moieties of l-TA on Ti-MOFs allow stereoselective recognition of guest molecules through hydrogen bonds formed between chiral cystine and the host. In a tris(2-carboxyethyl)phosphine hydrochloride-containing environment, the disulfide bonds in cystine molecules are further cleaved, and the HS-tails react with Fe(III) active sites, causing the loss of peroxidase-like performance of nanochannels. Benefitting from the nanochannel architecture's current-potential (I-V) properties, the selective recognition of cystine enantiomers is directly monitored through the peroxidase-like activity change-induced ionic current signatures. This study provides a new and universal strategy for distinguishing disulfide- and thiol-containing chiral molecules.

Rational design of mesoporous chiral MOFs as reactive pockets in nanochannels for enzyme-free identification of monosaccharide enantiomers.

Monosaccharides play significant roles in daily metabolism in living organisms. Although various devices have been constructed for monosaccharide identification, most rely on the specificity of the natural enzyme. Herein, inspired by natural ionic channels, an asymmetrical MOF-in-nanochannel architecture is developed to discriminate monosaccharide enantiomers based on cascade reactions by combining oxidase-mimicking and Fenton-like catalysis in homochiral mesoporous CuMOF pockets. The identification performance is remarkably enhanced by the increased oxidase-mimicking activity of Au nanoparticles under a local surface plasmon resonance (LSPR) excitation. The apparent steady-state kinetic parameters and nano-fluidic simulation indicate that the different affinities induced by Au-LSPR excitation and the confinement effect from MOF pockets precipitate the high chiral sensitivity. This study offers a promising strategy for designing an enantiomer discrimination device and helps to gain insight into the origin of stereoselectivity in a natural enzyme.

Long-term average spectrum measures of consecutive snore sounds from different sources determined by drug-induced sleep endoscopy.

STUDY OBJECTIVES: The goal of this study was to investigate the value of the long-term average spectrum in the acoustic analysis of snore sounds arising from different sources in the upper airway. METHODS: Long-term average spectrum was used to analyze sequences of 10 consecutive snore sounds that had been divided into 2 groups, soft-palate type and lateral-wall type, according to the vibration site generating the snore sounds and the patterns of soft tissue collapse in the upper airway as identified by drug-induced sleep endoscopy. We calculated the first spectral peak, mean spectral energy, high-frequency energy, 0-1 kHz spectral energy, 1-5 kHz spectral energy, and 0-1 kHz/1-5 kHz difference from each group and compared the differences between them. RESULTS: All parameters except mean spectral energy showed significant differences between the 2 groups. The first spectral peak of less than 265.53 Hz, and the 0-1k/1-5 kHz difference of less than -11.6 dB strongly suggests soft-palate-type snore sounds. CONCLUSIONS: Long-term average spectrum has potential application for snore sound source identification. We recommend using first spectral peak and a 0-1 kHz/1-5 Hz difference to identify soft-palate-type snore sounds. CITATION: Peng H, Xu H, Xu Z, Jia R, Yu H. Long-term average spectrum measures of consecutive snore sounds from different sources determined by drug-induced sleep endoscopy. J Clin Sleep Med. 2023;19(1):145-150.

Locally superengineered cascade recognition-quantification zones in nanochannels for sensitive enantiomer identification.

As an intriguing and intrinsic feature of life, chirality is highly associated with many significant biological processes. Simultaneous recognition and quantification of enantiomers remains a major challenge. Here, a sensitive enantiomer identification device is developed on TiO2 nanochannels via the design of cascade recognition-quantification zones along the nanochannels. In this system, ß-cyclodextrin (ß-CD) is self-assembled on one side of the nanochannels for the selective recognition of enantiomers; CuMOFs are designed as the target-responsive partners on the other side of the nanochannels for the quantification of enantiomers that pass through the nanochannels. As a proof-of-principle of the cascade design, arginine (Arg) enantiomers are tested as the identification targets. The l-Arg molecules selectively bind in the recognition zone; d-Arg molecules pass through the recognition zone and then interact with the quantification zone via a specialized reduction reaction. As verified by nanofluidic simulations, because of the confinement effect of nanoscale channels combined with the condensation effect of porous structure, the in situ reaction in the quantification zone contributes to an unprecedented variation in transmembrane K+ flux, leading to an improved identification signal. This novel cascade-zone nanochannel membrane provides a smart strategy to design multifunctional nanofluidic devices.

Target-Modulated Hydrophobic Precipitation in Photocatalytic Nanochannels for Sensitive Detection of Alpha Fetoprotein.

It is important to detect cancer biomarkers at an early stage of tumor development for the effective diagnosis and treatment of cancer. As a well-known probe for detecting superoxide (·O2-) radicals, nitro blue tetrazolium (NBT) can rapidly react with ·O2- to form a hydrophobic formazan precipitate. In this study, by deliberately utilizing this reaction, Pt asymmetrically decorated on a TiO2 nanochannel membrane (Pt/TiNM) is explored to fabricate an electrochemical immunosensing platform with outstanding selectivity and ultrahigh sensitivity. Using NBT as the substrate, hydrophobic formazan precipitation induces a substantial block of ionic diffusion flux in nanochannels. Using alpha fetoprotein (AFP) as the target analyte, the established immunorecognition event was used to induce MoS2-Ab2 conjugates. Thanks to the excellent light-shielding ability of MoS2 nanosheets, the production of ·O2- radicals from the photocatalysis of Pt/TiNM is effectively depressed because of the attenuated arrival of light. The reduced formazan precipitation results in ionic transport changes in nanochannels, which in turn enables the selective recognition of AFP down to 2 ng mL-1. This target-modulated sensing strategy is also capable of sensing other immune targets, thus paving a new way for designing nanochannel-based sensing platforms.

Nanoarchitectonics of a MOF-in-Nanochannel (HKUST-1/TiO2) Membrane for Multitarget Selective Enrichment and Staged Recovery.

Enrichment and separation of specific endogenous molecules are essential for disease diagnosis and the pharmaceutical industry. Although many solid sorbents have been developed for target molecule enrichment, simultaneous separation of multitargets is still a challenge for adsorbents. In this study, we develop a multitarget selective sorbent based on a nanochannel membrane prepared by the anodization of a Ti-Cu alloy. The in situ growth of a metal-organic framework (MOF, herein using Cu-based HKUST-1) in the nanochannels enables the resulting MOF-in-nanochannel membrane to act as a nanofilter. Benefitting from the size-exclusion effect of MOFs and the distinct surface characteristics of each component in the HKUST-1/TiO2 nanochannels, the as-proposed membranes can be simply operated as a filter and exhibit satisfactory selectivities and enrichment capacities in the separation of aromatic amino acids, histidine-rich proteins, and phosphoproteins. More importantly, the adsorbed multitargets can be further controllably released from the membrane in a sequence via a staged recovery process. The use of this system is envisioned to provide an innovative and potential design for efficient sorption media for the selective enrichment and staged separation of specific biomolecules.

Melanin-gelatin nanoparticles with both EPR effect and renal clearance for PA/MRI dual-modal imaging of tumors.

As cancer nanotherapeutics, the ideal multifunctional nanoparticles not only have the processing ability to accumulate effectively in tumors, but also can be excreted rapidly from the body via renal clearance after effective treatment. Melanin is an endogenous biological material, and gelatin has natural biocompatibility and biodegradability. Such materials are more promising in the development of diagnostic and therapeutic nanoplatform for future clinical translation. In this study, we have developed a kind of size-shrinkable PA/MRI theranostic agent based on gelatin fabricated ultrasmall melanin nanoparticles (MNPs-GNP). The MNPs-GNP nanoparticles, with a size of about 100 nm, presented good dispersibility, broadband light absorbance, negligible cellular cytotoxicity, preferable tumor accumulation by EPR-based passive targeting. The dual-modal imaging results showed that the nanoparticles have excellent photoacoustic (PA) imaging and nuclear magnetic resonance (MRI) imaging after tumor-bearing mice were intravenously injected with MNPs-GNP. Additionally, gelatin is the substrate of matrix metalloproteinase-2 (MMP-2), following with the degradation of gelatin nanoparticles by MMP-2, the large-size MNPs-GNP turns to be small-size melanin, which could mainly be excreted via renal clearance avoiding potential toxicity to body tissues. These preliminary results indicated that MNPs-GNP can overcome the dilemma between EPR and renal clearance, which has clinical application potentiality as a PA/MRI dual-modal candidate agent for cancer theranostics.

Sensor fault detection and minimum detectable fault analysis for dynamic point-the-bit rotary steerable system.

In this paper, the sensor fault detection problem considering the drilling disturbances is studied for the dynamic point-the-bit rotary steerable system. Firstly, the DPRSS is modeled as a linear system with the drilling disturbances, including unknown inputs, measurement noises, and model perturbations. Then, a finite-frequency zonotopic fault detection observer is proposed. The finite-frequency range H- performance and the P-radius criterion are considered to design the observer gains such that the residuals are sensitive to sensor faults and robust against the drilling disturbances simultaneously. Subsequently, the calculation method of minimum detectable faults is presented for the proposed sensor fault detection mechanism. Finally, simulations and experiments are presented to illustrate the effectiveness of the proposed methods.

Construction of Peroxidase-like Metal-Organic Frameworks in TiO2 Nanochannels: Robust Free-Standing Membranes for Diverse Target Sensing.

The high cost and easy denaturation of natural enzymes under environmental conditions hinder their practical usefulness in sensing devices. In this study, peroxidase (POD)-like metal-organic frameworks (MOFs) were in situ grown in the nanochannels of an anodized TiO2 membrane (TiO2NM) as an electrochemical platform for multitarget sensing. By directly using a nanochannel wall as the precursor of metal nodes, Ti-MOFs were in situ derived on the nanochannel wall. Benefitting from the presence of bipyridine groups on the ligands, the MOFs in the nanochannels provide plenty of sites for Fe3+ anchoring, thus endowing the resulting membrane (named as Fe3+:MOFs/TiO2NM) with remarkable POD-like activity. Such Fe3+-induced POD-like activity is very sensitive to thiol-containing molecules owing to the strong coordination effect of thiols on Fe3+. Most importantly, the POD-like activity of nanochannels can be in situ characterized by the current-potential (I-V) properties via catalyzing the oxidation of 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonate) (ABTS) substrate to the corresponding positively charged product ABTS•+. As a proof-of-concept application, the free-standing POD-like membranes were applied as a label-free assay in sensing cysteine, as well as monitoring acetylcholinesterase (AChE) activity through the generated thiol-containing product. Furthermore, based on the toxicity effect of organophosphorus (OP) compounds on AChE, the robust membranes were successfully utilized to evaluate the toxicity of diverse OP compounds. The POD-like nanochannels open up an innovative way to expand the application of nanochannel-based electrochemical sensing platforms in drug inspection, food safety, and environmental pollution.

Vibration pattern recognition using a compressed histogram of oriented gradients for snoring source analysis.

BACKGROUND: Snoring source analysis is essential for an appropriate surgical decision for both simple snorers and obstructive sleep apnea/hypopnea syndrome (OSAHS) patients. OBJECTIVE: As snoring sounds carry significant information about tissue vibrations within the upper airway, a new feature entitled compressed histogram of oriented gradients (CHOG) is proposed to recognize vibration patterns of the snoring source acoustically by compressing histogram of oriented gradients (HOG) descriptors via the multilinear principal component analysis (MPCA) algorithm. METHODS: Each vibration pattern corresponds to a sole or combinatorial vibration among the four upper airway soft tissues of soft palate, lateral pharyngeal wall, tongue base, and epiglottis. 1037 snoring events from noncontact sound recordings of 76 simple snorers or OSAHS patients during drug-induced sleep endoscopy (DISE) were evaluated. RESULTS: With a support vector machine (SVM) as the classifier, the proposed CHOG achieved a recognition accuracy of 89.8% for the seven observable vibration patterns of the snoring source categorized in our most recent work. CONCLUSION: The CHOG outperforms other single features widely used for acoustic analysis of sole vibration site.

Asymmetric coupling of Au nanospheres on TiO2 nanochannel membranes for NIR-gated artificial ionic nanochannels.

Au nanoparticles were asymmetrically fabricated at one tip of TiO2 nanochannels by combining a photocatalytic reaction and limited penetration of light. Using the asymmetrical nanochannel-based membrane as a plasma absorber, near-infrared-gated artificial ionic nanochannels were designed.

Nucleation and dissociation of methane clathrate embryo at the gas-water interface.

Among natural energy resources, methane clathrate has attracted tremendous attention because of its strong relevance to current energy and environment issues. Yet little is known about how the clathrate starts to nucleate and disintegrate at the molecular level, because such microscopic processes are difficult to probe experimentally. Using surface-specific sum-frequency vibrational spectroscopy, we have studied in situ the nucleation and disintegration of methane clathrate embryos at the methane-gas-water interface under high pressure and different temperatures. Before appearance of macroscopic methane clathrate, the interfacial structure undergoes 3 stages as temperature varies, namely, dissolution of methane molecules into water interface, formation of cage-like methane-water complexes, and appearance of microscopic methane clathrate, while the bulk water structure remains unchanged. We find spectral features associated with methane-water complexes emerging in the induction time. The complexes are present over a wide temperature window and act as nuclei for clathrate growth. Their existence in the melt of clathrates explains why melted clathrates can be more readily recrystallized at higher temperature, the so-called "memory effect." Our findings here on the nucleation mechanism of clathrates could provide guidance for rational control of formation and disintegration of clathrates.

Biomineralization-Driven Ion Gate in TiO2 Nanochannel Arrays for Cell H2S Sensing.

The level of hydrogen sulfide in the brain and vasculature has long been associated with human health and diseases. Hence, simple and robust analytical tools allowing determination of hydrogen sulfide levels are highly desirable. In this work, a biomineralization-driven ion gate in TiO2 nanochannel arrays for H2S sensing was designed and developed. The formed CuS precipitation decreased the transmembrane current in the presence of bovine serum albumin used as biological mineralizer. Label-free assay for sensing of intracellular S2- was achieved based on changes in ionic current with a detection limit of 56 MCF-7 cells. More importantly, the proposed sensing strategy was promising for reusable application through dissolution of CuS in an acidic media (pH = 1).

Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction.

The advent of 'Big Data' and 'Deep Learning' offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for 'feeding' the subsequent classifiers. With increasing numbers of biomedical data, extracting features from these 'big' data is an intensive and time-consuming task. In this case study, we employ a Graphics Processing Unit (GPU) via Python to extract features from a large corpus of snore sound data. Those features can subsequently be imported into many well-known deep learning training frameworks without any format processing. The snore sound data were collected from several hospitals (20 subjects, with 770-990 MB per subject - in total 17.20 GB). Experimental results show that our GPU-based processing significantly speeds up the feature extraction phase, by up to seven times, as compared to the previous CPU system.