Quantitative Analysis of the Membrane Affinity of Local Anesthetics Using a Model Cell Membrane.

Local anesthesia is a drug that penetrates the nerve cell membrane and binds to the voltage gate sodium channel, inhibiting the membrane potential and neurotransmission. It is mainly used in clinical uses to address the pain of surgical procedures in the local area. Local anesthetics (LAs), however, can be incorporated into the membrane, reducing the thermal stability of the membrane as well as altering membrane properties such as fluidity, permeability, and lipid packing order. The effects of LAs on the membrane are not yet fully understood, despite a number of previous studies. In particular, it is necessary to analyze which is the more dominant factor, the membrane affinity or the structural perturbation of the membrane. To analyze the effects of LAs on the cell membrane and compare the results with those from model membranes, morphological analysis and 50% inhibitory concentration (IC50) measurement of CCD-1064sk (fibroblast, human skin) membranes were carried out for lidocaine (LDC) and tetracaine (TTC), the most popular LAs in clinical use. Furthermore, the membrane affinity of the LAs was quantitatively analyzed using a colorimetric polydiacetylene assay, where the color shift represents their distribution in the membrane. Further, to confirm the membrane affinity and structural effects of the membranes, we performed an electrophysiological study using a model protein (gramicidin A, gA) and measured the channel lifetime of the model protein on the free-standing lipid bilayer according to the concentration of each LA. Our results show that when LAs interact with cell membranes, membrane affinity is a more dominant factor than steric or conformational effects of the membrane

Revisiting the potential of regulated cell death in glioma treatment: a focus on autophagy-dependent cell death, anoikis, ferroptosis, cuproptosis, pyroptosis, immunogenic cell death, and the crosstalk between them

Gliomas are primary tumors that originate in the central nervous system. The conventional treatment options for gliomas typically encompass surgical resection and temozolomide (TMZ) chemotherapy. However, despite aggressive interventions, the median survival for glioma patients is merely about 14.6 months. Consequently, there is an urgent necessity to explore innovative therapeutic strategies for treating glioma. The foundational study of regulated cell death (RCD) can be traced back to Karl Vogt’s seminal observations of cellular demise in toads, which were documented in 1842. In the past decade, the Nomenclature Committee on Cell Death (NCCD) has systematically classified and delineated various forms and mechanisms of cell death, synthesizing morphological, biochemical, and functional characteristics. Cell death primarily manifests in two forms: accidental cell death (ACD), which is caused by external factors such as physical, chemical, or mechanical disruptions; and RCD, a gene-directed intrinsic process that coordinates an orderly cellular demise in response to both physiological and pathological cues. Advancements in our understanding of RCD have shed light on the manipulation of cell death modulation - either through induction or suppression - as a potentially groundbreaking approach in oncology, holding significant promise. However, obstacles persist at the interface of research and clinical application, with significant impediments encountered in translating to therapeutic modalities. It is increasingly apparent that an integrative examination of the molecular underpinnings of cell death is imperative for advancing the field, particularly within the framework of inter-pathway functional synergy. In this review, we provide an overview of various forms of RCD, including autophagy-dependent cell death, anoikis, ferroptosis, cuproptosis, pyroptosis and immunogenic cell death. We summarize the latest advancements in understanding the molecular mechanisms that regulate RCD in glioma and explore the interconnections between different cell death processes. By comprehending these connections and developing targeted strategies, we have the potential to enhance glioma therapy through manipulation of RCD

Aptamer-Conjugated Polydiacetylene Colorimetric Paper Chip for the Detection of Bacillus thuringiensis Spores

A colorimetric polydiacetylene (PDA) paper strip sensor that can specifically recognize Bacillus thuringiensis (BT) HD-73 spores is described in this work. The target-specific aptamer was combined with PDA, and the aptamer-conjugated PDA vesicles were then coated on polyvinylidene fluoride (PVDF) paper strips by a simple solvent evaporation method. The PDA-aptamer paper strips can be used to detect the target without any pre-treatment. Using the paper strip, the presence of BT spores is directly observable by the naked eye based on the unique blue-to-red color transition of the PDA. Quantitative studies using the paper strip were also carried out by analyzing the color transitions of the PDA. The specificity of this PDA sensor was verified with a high concentration of Escherichia coli, and no discernable change was observed. The observable color change in the paper strip occurs in less than 1 h, and the limit of detection is 3 × 107 CFU/mL, much below the level harmful to humans. The PDA-based paper sensor, developed in this work, does not require a separate power or detection device, making the sensor strip highly transportable and suitable for spore analysis anytime and anywhere. Moreover, this paper sensor platform is easily fabricated, can be adapted to other targets, is highly portable, and is highly specific for the detection of BT spores

Federated Learning Based Fault Diagnosis Driven by Intra-Client Imbalance Degree

Federated learning is an effective means to combine model information from different clients to achieve joint optimization when the model of a single client is insufficient. In the case when there is an inter-client data imbalance, it is significant to design an imbalanced federation aggregation strategy to aggregate model information so that each client can benefit from the federation global model. However, the existing method has failed to achieve an efficient federation strategy in the case when there is an imbalance mode mismatch between clients. This paper aims to design a federated learning method guided by intra-client imbalance degree to ensure that each client can receive the maximum benefit from the federation model. The degree of intra-client imbalance, measured by gain of a class-by-class model update on the federation model based on a small balanced dataset, is used to guide the designing of federation strategy. An experimental validation for the benchmark dataset of rolling bearing shows that a 23.33% improvement of fault diagnosis accuracy can be achieved in the case when the degree of imbalance mode mismatch between clients is prominent

Trend Feature Consistency Guided Deep Learning Method for Minor Fault Diagnosis

Deep learning can be applied in the field of fault diagnosis without an accurate mechanism model. However, the accurate diagnosis of minor faults using deep learning is limited by the training sample size. In the case that only a small number of noise-polluted samples is available, it is crucial to design a new learning mechanism for the training of deep neural networks to make it more powerful in feature representation. The new learning mechanism for deep neural networks model is accomplished by designing a new loss function such that accurate feature representation driven by consistency of trend features and accurate fault classification driven by consistency of fault direction both can be secured. In such a way, a more robust and more reliable fault diagnosis model using deep neural networks can be established to effectively discriminate those faults with equal or similar membership values of fault classifiers, which is unavailable for traditional methods. Validation for gearbox fault diagnosis shows that 100 training samples polluted with strong noise are adequate for the proposed method to successfully train deep neural networks to achieve satisfactory fault diagnosis accuracy, while more than 1500 training samples are required for traditional methods to achieve comparative fault diagnosis accuracy

Multi-Scale Recursive Semi-Supervised Deep Learning Fault Diagnosis Method with Attention Gate

The efficiency of deep learning-based fault diagnosis methods for bearings is affected by the sample size of the labeled data, which might be insufficient in the engineering field. Self-training is a commonly used semi-supervised method, which is usually limited by the accuracy of features for unlabeled data screening. It is significant to design an efficient training mechanism to extract accurate features and a novel feature fusion mechanism to ensure that the fused feature is capable of screening. A novel training mechanism of multi-scale recursion (MRAE) is designed for Autoencoder in this article, which can be used for accurate feature extraction with a small amount of labeled data. An attention gate-based fusion mechanism was constructed to make full use of all useful features in the sense that it can incorporate distinguishing features on different scales. Utilizing large numbers of unlabeled data, the proposed multi-scale recursive semi-supervised deep learning fault diagnosis method with attention gate (MRAE-AG) can efficiently improve the fault diagnosis performance of DNNs trained by a small number of labeled data. A benchmark dataset from the Case Western Reserve University bearing data center was used to validate this novel method which shows that 7.76% accuracy improvement can be achieved in the case when only 10 labeled samples was available for supervised training of the DNN-based fault diagnosis model

Two new brown rot polypores from tropical China

Brown-rot fungi are types of fungi that selectively degrade cellulose and hemicellulose from wood and are perhaps the most important agents involved in the degradation of wood products and dead wood in forest ecosystem. Two new brown-rot species, collected from southern China, are nested within the clades of Fomitopsis sensu stricto and Oligoporus sensu stricto, respectively. Their positions are strongly supported in the Maximum Likelihood phylogenetic tree of the concatenated the internal transcribed spacer (ITS) regions, the large subunit of nuclear ribosomal RNA gene (nLSU), the small subunit of nuclear ribosomal RNA gene (nuSSU), the small subunit of mitochondrial rRNA gene (mtSSU), the largest subunit of RNA polymerase II (RPB1), the second largest subunit of RNA polymerase II (RPB2) and the translation elongation factor 1-α gene (TEF1) sequences. Fomitopsis bambusae, only found on bamboo, is characterised by its resupinate to effused-reflexed or pileate basidiocarps, small pores (6–9 per mm), the absence of cystidia, short cylindrical to oblong-ellipsoid basidiospores measuring 4.2–6.1 × 2–2.3 μm. Oligoporus podocarpi is characterised by white to pale cream pore surface, round or sometimes angular pores (5–6 per mm), broadly ellipsoid to reniform basidiospores measuring 3.8–4.2 × 2–2.3 μm and growing on Podocarpus. Illustrated descriptions of these two novel species, Fomitopsis bambusae and Oligoporus podocarpi, are provided