Dexmedetomidine improves the circulatory dysfunction of the glymphatic system induced by sevoflurane through the PI3K/AKT/ΔFosB/AQP4 pathway in young mice.

Multiple sevoflurane exposures may damage the developing brain. The neuroprotective function of dexmedetomidine has been widely confirmed in animal experiments and human studies. However, the effect of dexmedetomidine on the glymphatic system has not been clearly studied. We hypothesized that dexmedetomidine could alleviate sevoflurane-induced circulatory dysfunction of the glymphatic system in young mice. Six-day-old C57BL/6 mice were exposed to 3% sevoflurane for 2 h daily, continuously for 3 days. Intraperitoneal injection of either normal saline or dexmedetomidine was administered before every anaesthesia. Meanwhile the circulatory function of glymphatic system was detected by tracer injection at P8 and P32. On P30-P32, behavior tests including open field test, novel object recognition test, and Y-maze test were conducted. Primary astrocyte cultures were established and treated with the PI3K activator 740Y-P, dexmedetomidine, and small interfering RNA (siRNA) to silence ΔFosB. We propose for the first time that multiple exposure to sevoflurane induces circulatory dysfunction of the glymphatic system in young mice. Dexmedetomidine improves the circulatory capacity of the glymphatic system in young mice following repeated exposure to sevoflurane through the PI3K/AKT/ΔFosB/AQP4 signaling pathway, and enhances their long-term learning and working memory abilities.

Long-Term Detection of Glycemic Glucose/Hypoglycemia by Microfluidic Sweat Monitoring Patch.

A microfluidic sweat monitoring patch that collects human sweat for a long time is designed to achieve the effect of detecting the rise and fall of human sweat glucose over a long period of time by increasing the use time of a single patch. Five collection pools, four serpentine channels, and two different valves are provided. Among them, the three-dimensional valve has a large burst pressure as a balance between the internal and external air pressures of the patch. The bursting pressure of the two-dimensional diverter valve is smaller than that of the three-dimensional gas valve, and its role is to control the flow direction of the liquid. Through plasma hydrophilic treatment of different durations, the optimal hydrophilic duration is obtained. The embedded chromogenic disc detects the sweat glucose value at two adjacent time intervals and compares the information of the human body to increase or reduce glucose. The patch has good flexibility and can fit well with human skin, and because polydimethylsiloxane (PDMS) has good light transmission, it reduces the measurement error caused by the color-taking process and makes the detection results more accurate.

Comparative effectiveness of sodium-glucose cotransporter-2 inhibitors for new-onset gastric cancer and gastric diseases in patients with type 2 diabetes mellitus: a population-based cohort study.

OBJECTIVE: To compare the risks of gastric cancer and other gastric diseases in patients with type-2 diabetes mellitus (T2DM) exposed to sodium-glucose cotransporter 2 inhibitors (SGLT2I), dipeptidyl peptidase-4 inhibitors (DPP4I) or glucagon-like peptide-1 receptor agonists (GLP1a). DESIGN: This was a population-based cohort study of prospectively collected data on patients with T2DM prescribed SGLT2I, DPP4I or GLP1a between January 1st 2015 and December 31st 2020 from Hong Kong. The outcomes were new-onset gastric cancer, peptic ulcer (PU), acute gastritis, non-acute gastritis, and gastroesophageal reflux disease (GERD). Propensity score matching (1:1) using the nearest neighbour search was performed, and multivariable Cox regression was applied. A three-arm comparison between SGLT2I, DPP4I and GLP1a was conducted using propensity scores with inverse probability of treatment weighting. RESULTS: A total of 62,858 patients (median age: 62.2 years old [SD: 12.8]; 55.93% males; SGLT2I: n = 23,442; DPP4I: n = 39,416) were included. In the matched cohort, the incidence of gastric cancer was lower in SGLT2I (Incidence rate per 1000 person-year, IR: 0.32; 95% confidence interval, CI 0.23-0.43) than in DPP4I (IR per 1000 person-year: 1.22; CI 1.03-1.42) users. Multivariable Cox regression found that SGLT2I use was associated with lower risks of gastric cancer (HR 0.30; 95% CI 0.19-0.48), PU, acute gastritis, non-acute gastritis, and GERD (p < 0.05) compared to DPP4I use. In the three-arm analysis, GLP1a use was associated with higher risks of gastric cancer and GERD compared to SGLT2I use. CONCLUSIONS: The use of SGLT2I was associated with lower risks of new-onset gastric cancer, PU, acute gastritis, non-acute gastritis, and GERD after matching and adjustments compared to DPP4I use. SGLT2I use was associated with lower risks of GERD and gastric cancer compared to GLP1a use.

Quantitative proteome-wide O-glycoproteomics analysis with FragPipe.

Identification of O-glycopeptides from tandem mass spectrometry data is complicated by the near complete dissociation of O-glycans from the peptide during collisional activation and by the combinatorial explosion of possible glycoforms when glycans are retained intact in electron-based activation. The recent O-Pair search method provides an elegant solution to these problems, using a collisional activation scan to identify the peptide sequence and total glycan mass, and a follow-up electron-based activation scan to localize the glycosite(s) using a graph-based algorithm in a reduced search space. Our previous O-glycoproteomics methods with MSFragger-Glyco allowed for extremely fast and sensitive identification of O-glycopeptides from collisional activation data but had limited support for site localization of glycans and quantification of glycopeptides. Here, we report an improved pipeline for O-glycoproteomics analysis that provides proteome-wide, site-specific, quantitative results by incorporating the O-Pair method as a module within FragPipe. In addition to improved search speed and sensitivity, we add flexible options for oxonium ion-based filtering of glycans and support for a variety of MS acquisition methods and provide a comparison between all software tools currently capable of O-glycosite localization in proteome-wide searches.

Deamidation enables pathogenic SMAD6 variants to activate the BMP signaling pathway.

The BMP signaling pathway plays a crucial role in regulating early embryonic development and tissue homeostasis. SMAD6 encodes a negative regulator of BMP, and rare variants of SMAD6 are recurrently found in individuals with birth defects. However, we observed that a subset of rare pathogenic variants of SMAD6 consistently exhibited positive regulatory effects instead of the initial negative effects on the BMP signaling pathway. We sought to determine whether these SMAD6 variants have common pathogenic mechanisms. Here, we showed that pathogenic SMAD6 variants accompanying this functional reversal exhibit similar increases in deamidation. Mechanistically, increased deamidation of SMAD6 variants promotes the accumulation of the BMP receptor BMPR1A and the formation of new complexes, both of which lead to BMP signaling pathway activation. Specifically, two residues, N262 and N404, in SMAD6 were identified as the crucial sites of deamidation, which was catalyzed primarily by glutamine-fructose-6-phosphate transaminase 2 (GFPT2). Additionally, treatment of cells harboring SMAD6 variants with a deamidase inhibitor restored the inhibitory effect of SMAD6 on the BMP signaling pathway. Conversely, when wild-type SMAD6 was manually simulated to mimic the deamidated state, the reversed function of activating BMP signaling was reproduced. Taken together, these findings show that deamidation of SMAD6 plays a crucial role in the functional reversal of BMP signaling activity, which can be induced by a subset of various SMAD6 variants. Our study reveals a common pathogenic mechanism shared by these variants and provides a potential strategy for preventing birth defects through deamidation regulation, which might prevent the off-target effects of gene editing.

Synergistic stabilization of a menthol Pickering emulsion by zein nanoparticles and starch nanocrystals: Preparation, structural characterization, and functional properties.

A Pickering emulsion was synergistically stabilised with zein nanoparticles (ZNPs) and starch nanocrystals (SNCs) to prepare it for menthol loading. After response surface optimisation of the emulsion preparation conditions, a Pickering emulsion prepared with a ZNPs:SNCs ratio of 1:1, a particle concentration of 2 wt% and a water:oil ratio of 1:1 provided the highest menthol encapsulation rate of the emulsions tested (83%) with good storage stability within 30 days. We examined the bilayer interface structure of the emulsion by optical microscopy, scanning electron microscopy, and confocal laser scanning microscopy. The results of simulated digestion experiments showed that the release rate of free fatty acid was 75.06 ± 1.23%, which ensured bioavailability. At the same time, the emulsions facilitated the slow release of menthol. Bacteriostatic studies revealed that the Pickering emulsion had a protective effect on menthol, with the most significant inhibitory effects on Escherichia coli and Staphylococcus aureus under the same conditions. Overall, this study proposes a novel approach for the application and development of l-menthol by combining it with Pickering emulsion.

Reconstruction of transparent objects using phase shifting profilometry based on diffusion models.

Phase shifting profilometry is an important technique for reconstructing the three-dimensional (3D) geometry of objects with purely diffuse surfaces. However, it is challenging to measure the transparent objects due to the pattern aliasing caused by light refraction and multiple reflections inside the object. In this work, we analyze the aliasing fringe pattern formation for transparent objects and then, propose to learn the front surface light intensity distribution based on the formation principle by using the diffusion models for generating the non-aliased fringe patterns reflected from the front surface only. With the generated fringe patterns, the 3D shape of the transparent objects can be reconstructed via the conventional structured light. We show the feasibility and performance of the proposed method on the data of purely transparent objects that are not seen in the training stage. Moreover, we found it could be generalized to other cases with local-transparent and translucent objects, showing the potential capability of the diffusion based learnable framework in tackling the problems of transparent object reconstruction.

Advances in the application of auxiliary imaging techniques in parathyroid diseases.

Hypoparathyroidism is one of the main complications after total thyroidectomy, severely affecting patients' quality of life. How to effectively protect parathyroid function after surgery and reduce the incidence of hypoparathyroidism has always been a key research area in thyroid surgery. Therefore, precise localization of parathyroid glands during surgery, effective imaging, and accurate surgical resection have become hot topics of concern for thyroid surgeons. In response to this clinical phenomenon, this study compared several different imaging methods for parathyroid surgery, including nanocarbon, indocyanine green, near-infrared imaging techniques, and technetium-99m methoxyisobutylisonitrile combined with gamma probe imaging technology. The advantages and disadvantages of each method were analyzed, providing scientific recommendations for future parathyroid imaging. In recent years, some related basic and clinical research has also been conducted in thyroid surgery. This article reviewed relevant literature and provided an overview of the practical application progress of various imaging techniques in parathyroid surgery.

Halogen Bonding in Perfluoroalkyl Adsorption.

Adsorption is a promising technology to remove perfluoroalkyl substances (PFAS), including perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA), from contaminated water. Although a large number of materials have been evaluated for PFAS adsorption, guidelines that can facilitate the rational design and selection of adsorbents have not been established due to the lack of a mechanistic understanding on the molecular level. Using a novel interpretation of the Freundlich isotherm, this study identifies halogen bonding as the main mechanism controlling perfluoroalkyl adsorption by using a materiomic approach that compares the electrostatic polarities of a variety of carbon, polymer, and mineral-based materials reported in the literature. Comparisons show that both PFOS and PFOA are favorably adsorbed by materials containing high densities of π electrons, lone electron pairs, and negative charges, consistent with the formation of halogen bonding between the positive σ-hole of fluorine as a Lewis acid and a nucleophilic solid as a Lewis base. The identification of this previously unappreciated noncovalent bonding mechanism offers fresh insight into the search of suitable materials for perfluoroalkyl adsorption.

Mussel byssus-inspired dual-functionalization of zirconia dental implants for improved bone integration.

Zirconia faces challenges in dental implant applications due to its inherent biological inertness, which compromises osseointegration, a critical factor for the long-term success of implants that rely heavily on specific cell adhesion and enhanced osteogenic activity. Here, we fabricated a dual-functional coating that incorporates strontium ions, aimed at enhancing osteogenic activity, along with an integrin-targeting sequence to improve cell adhesion by mussel byssus-inspired surface chemistry. The results indicated that although the integrin-targeting sequence at the interface solely enhances osteoblast adhesion without directly increasing osteogenic activity, its synergistic interaction with the continuously released strontium ions from the coating, as compared to the release of strontium ions alone, significantly enhances the overall osteogenic effect. More importantly, compared to traditional polydopamine surface chemistry, the coating surface is enriched with amino groups capable of undergoing various chemical reactions and exhibits enhanced stability and aesthetic appeal. Therefore, the synergistic interplay between strontium and the functionally customizable surface offers considerable potential to improve the success of zirconia implantation.

The interaction of endorepellin and neurexin triggers neuroepithelial autophagy and maintains neural tube development.

Heparan sulfate proteoglycan 2 (HSPG2) gene encodes the matrix protein Perlecan, and genetic inactivation of this gene creates mice that are embryonic lethal with severe neural tube defects (NTDs). We discovered rare genetic variants of HSPG2 in 10% cases compared to only 4% in controls among a cohort of 369 NTDs. Endorepellin, a peptide cleaved from the domain V of Perlecan, is known to promote angiogenesis and autophagy in endothelial cells. The roles of enderepellin in neurodevelopment remain unclear so far. Our study revealed that endorepellin can migrate to the neuroepithelial cells and then be recognized and bind with the neuroepithelia receptor neurexin in vivo. Through the endocytic pathway, the interaction of endorepellin and neurexin physiologically triggers autophagy and appropriately modulates the differentiation of neural stem cells into neurons as a blocker, which is necessary for normal neural tube closure. We created knock-in (KI) mouse models with human-derived HSPG2 variants, using sperm-like stem cells that had been genetically edited by CRISPR/Cas9. We realized that any HSPG2 variants that affected the function of endorepellin were considered pathogenic causal variants for human NTDs given that the severe NTD phenotypes exhibited by these KI embryos occurred in a significantly higher response frequency compared to wildtype embryos. Our study provides a paradigm for effectively confirming pathogenic mutations in other genetic diseases. Furthermore, we demonstrated that using autophagy inhibitors at a cellular level can repress neuronal differentiation. Therefore, autophagy agonists may prevent NTDs resulting from failed autophagy maintenance and neuronal over-differentiation caused by deleterious endorepellin variants.

Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification.

Aims: Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence (AI) to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. We hypothesize that AI models with a specific design can provide fine-grained interpretation of ECGs to advance cardiovascular diagnosis, stratify mortality risks, and identify new clinically useful information. Methods and results: Utilizing a data set of 2 322 513 ECGs collected from 1 558 772 patients with 7 years follow-up, we developed a deep-learning model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hypertension screening solely from ECGs, which are then used to stratify the risk of mortality. The model achieved the area under the receiver operating characteristic curve (AUC) scores of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (95% CI, 0.963-0.965), and 0.839 (95% CI, 0.837-0.841) for the three diagnostic tasks separately. Using ECG-predicted results, we find high risks of mortality for subjects with sinus tachycardia (adjusted hazard ratio (HR) of 2.24, 1.96-2.57), and atrial fibrillation (adjusted HR of 2.22, 1.99-2.48). We further use salient morphologies produced by the deep-learning model to identify key ECG leads that achieved similar performance for the three diagnoses, and we find that the V1 ECG lead is important for hypertension screening and mortality risk stratification of hypertensive cohorts, with an AUC of 0.816 (0.814-0.818) and a univariate HR of 1.70 (1.61-1.79) for the two tasks separately. Conclusion: Using ECGs alone, our developed model showed cardiologist-level accuracy in interpretable cardiac diagnosis and the advancement in mortality risk stratification. In addition, it demonstrated the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available.

Ketamine administration causes cognitive impairment by destroying the circulation function of the glymphatic system.

BACKGROUND: Ketamine, as a non-competitive antagonist of N-methyl-D-aspartate (NMDA) receptors, was originally used in general anesthesia. Epidemiological data show that ketamine has become one of the most commonly abused drugs in China. Ketamine administration might cause cognitive impairment; however, its molecular mechanism remains unclear. The glymphatic system is a lymphoid system that plays a key role in metabolic waste removal and cognitive regulation in the central nervous system. METHODS: Focusing on the glymphatic system, this study evaluated the behavioral performance and circulatory function of the glymphatic system by building a short-term ketamine administration model in mice, and detected the expression levels of the 5-HT2c receptor, ΔFosb, Pten, Akt, and Aqp4 in the hippocampus. Primary astrocytes were cultured to verify the regulatory relationships among related indexes using a 5-HT2c receptor antagonist, a 5-HT2c receptor short interfering RNA (siRNA), and a ΔFosb siRNA. RESULTS: Ketamine administration induced ΔFosb accumulation by increasing 5-HT2c receptor expression in mouse hippocampal astrocytes and primary astrocytes. ΔFosb acted as a transcription factor to recognize the AATGATTAAT bases in the 5' regulatory region of the Aqp4 gene (-1096 bp to -1087 bp), which inhibited Aqp4 expression, thus causing the circulatory dysfunction of the glymphatic system, leading to cognitive impairment. CONCLUSIONS: Although this regulatory mechanism does not involve the Pten/Akt pathway, this study revealed a new mechanism of ketamine-induced cognitive impairment in non-neuronal systems, and provided a theoretical basis for the safety of clinical treatment and the effectiveness of withdrawal.

Stable CO2reduction under natural air on Ni-Sn hydroxide photocatalyst with dynamic renewable oxygen vacancies.

Advanced photocatalysts are highly desired to activate the photocatalytic CO2reduction reaction (CO2RR) with low concentration. Herein, the NiSn(OH)6with rich surface lattice hydroxyls was synthesized to boost the activity directly under the natural air. Results showed that terminal Ni-OH could serve as donors to feed protons and generate oxygen vacancies (VO), thus beneficial to convert the activated CO2(HCO3-) mainly into CO (5.60µmol g-1) in the atmosphere. It was flexible and widely applicable for a stable CO2RR from high pure to air level free of additionally adding H2O reactant, and higher than the traditional gas-liquid-solid (1.58µmol g-1) and gas-solid (4.07µmol g-1) reaction system both using high pure CO2and plenty of H2O. The strong hydrophilia by the rich surface hydroxyls allowed robust H2O molecule adsorption and dissociation at VOsites to achieve the Ni-OH regeneration, leading to a stable CO yield (11.61µmol g-1) with the enriched renewable VOregardless of the poor CO2and H2O in air. This work opens up new possibilities for the practical application of natural photosynthesis.

Deep reinforcement learning for multi-class imbalanced training: applications in healthcare.

With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the context of clinical data, where there may be one rare event for many cases in the majority class. We introduce an imbalanced classification framework, based on reinforcement learning, for training extremely imbalanced data sets, and extend it for use in multi-class settings. We combine dueling and double deep Q-learning architectures, and formulate a custom reward function and episode-training procedure, specifically with the capability of handling multi-class imbalanced training. Using real-world clinical case studies, we demonstrate that our proposed framework outperforms current state-of-the-art imbalanced learning methods, achieving more fair and balanced classification, while also significantly improving the prediction of minority classes. Supplementary Information: The online version contains supplementary material available at 10.1007/s10994-023-06481-z.

Bisphenol A Exposure Interferes with Reproductive Hormones and Decreases Sperm Counts: A Systematic Review and Meta-Analysis of Epidemiological Studies.

Bisphenol A (BPA), an acknowledged endocrine disrupter, is easily exposed to humans via food packaging and container. However, a consensus has not been reached on the extent to which BPA exposure affects the reproductive system. We therefore conducted this systematic review and meta-analysis to elucidate the relationship between BPA exposure and male reproduction-related indicators. Up to October 2023, a comprehensive search was carried out in the PubMed, Embase, Cochrane and Web of Science, and 18 studies were ultimately included. ß coefficients from multivariate linear regression analyses were pooled using a random effects model. The results showed that the urinary BPA concentration was negatively correlated with the sperm concentration (ß coefficient = -0.03; 95% CI: -0.06 to -0.01; I2 = 0.0%, p = 0.003) and total sperm count (ß coefficient = -0.05; 95% CI: -0.08 to -0.02; I2 = 0.0%, p < 0.001). In addition, BPA concentrations were associated with increased sex hormone-binding globulin (SHBG) levels, increased estradiol (E2) levels, and reduced biologically active androgen levels. However, the relationship between an increased risk of below-reference sperm quality and BPA exposure was not robust. This systematic review revealed that BPA exposure disrupts reproductive hormones, reduces sperm counts and may ultimately adversely affect male reproduction.

De novo design of drug-binding proteins with predictable binding energy and specificity.

The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule-binding proteins with tuned interaction energies is feasible entirely from computation.

Hyperthermia improves gemcitabine sensitivity of pancreatic cancer cells by suppressing the EFNA4/ß-catenin axis and activating dCK.

Background: Previously, our investigations have underscored the potential of hyperthermia to improve the therapeutic efficacy of gemcitabine (GEM) in pancreatic cancer (PC). Nonetheless, the precise underlying mechanisms remain elusive. Methods: We engineered two GEM-resistant PC cell lines (BxPC-3/GEM and PANC-1/GEM) and treated them with GEM alongside hyperthermia. The impact of hyperthermia on the therapeutic potency of GEM was ascertained through MTT assay, assessment of the concentration of its active metabolite dFdCTP, and evaluation of deoxycytidine kinase (dCK) activity. Lentivirus-mediated dCK silencing was further employed to validate its involvement in mediating the GEM-sensitizing effect of hyperthermia. The mechanism underlying hyperthermia-mediated dCK activation was explored using bioinformatics analyses. The interplay between hyperthermia and the ephrin A4 (EFNA4)/ß-catenin/dCK axis was investigated, and their roles in GEM resistance was further explored via the establishment of xenograft tumor models in nude mice. Results: Hyperthermia restored dCK expression in GEM-resistant cell lines, concurrently enhancing GEM sensitivity and fostering DNA damage and cell death. These observed effects were negated by dCK silencing. Regarding the mechanism, hyperthermia activated dCK by downregulating EFNA4 expression and mitigating ß-catenin activation. Overexpression of EFNA4 activated the ß-catenin while suppressing dCK, thus diminishing cellular GEM sensitivity-a phenomenon remediated by the ß-catenin antagonist MSAB. Consistently, in vivo, hyperthermia augmented the therapeutic efficacy of GEM on xenograft tumors through modulation of the ephrin A4/ß-catenin/dCK axis. Conclusion: This study delineates the role of hyperthermia in enhancing GEM sensitivity of PC cells, primarily mediated through the suppression of the EFNA4/ß-catenin axis and activation of dCK.

AutoNet-Generated Deep Layer-Wise Convex Networks for ECG Classification.

The design of neural networks typically involves trial-and-error, a time-consuming process for obtaining an optimal architecture, even for experienced researchers. Additionally, it is widely accepted that loss functions of deep neural networks are generally non-convex with respect to the parameters to be optimised. We propose the Layer-wise Convex Theorem to ensure that the loss is convex with respect to the parameters of a given layer, achieved by constraining each layer to be an overdetermined system of non-linear equations. Based on this theorem, we developed an end-to-end algorithm (the AutoNet) to automatically generate layer-wise convex networks (LCNs) for any given training set. We then demonstrate the performance of the AutoNet-generated LCNs (AutoNet-LCNs) compared to state-of-the-art models on three electrocardiogram (ECG) classification benchmark datasets, with further validation on two non-ECG benchmark datasets for more general tasks. The AutoNet-LCN was able to find networks customised for each dataset without manual fine-tuning under 2 GPU-hours, and the resulting networks outperformed the state-of-the-art models with fewer than 5% parameters on all the above five benchmark datasets. The efficiency and robustness of the AutoNet-LCN markedly reduce model discovery costs and enable efficient training of deep learning models in resource-constrained settings.