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Intelligent fault diagnostics based on deep learning provides a favorable guarantee for the reliable operation of equipment, but a trained deep learning model generally has low prediction accuracy in cross-domain diagnostics. To solve this problem, a deep learning fault diagnosis method based on the reconstructed envelope spectrum is proposed to improve the ability of rolling bearing cross-domain fault diagnostics in this paper. First, based on the envelope spectrum morphology of rolling bearing failures, a standard envelope spectrum is constructed that reveals the unique characteristics of different bearing health states and eliminates the differences between domains due to different bearing speeds and bearing models. Then, a fault diagnosis model was constructed using a convolutional neural network to learn features and complete fault classification. Finally, using two publicly available bearing data sets and one bearing data set obtained by self-experimentation, the proposed method is applied to the data of the fault diagnostics of rolling bearings under different rotational speeds and different bearing types. The experimental results show that, compared with some popular feature extraction methods, the proposed method can achieve high diagnostic accuracy with data at different rotational speeds and different bearing types, and it is an effective method for solving the problem with cross-domain fault diagnostics for rolling bearings.

Optical remote sensing is one of the most attractive options for generating crop cover maps because it enables computation of vegetation indices, which are useful for assessing the condition of vegetation. The Sentinel-2A Multispectral Instrument (MSI), which is a multispectral sensor with 13 bands covering the visible, near infrared and short-wave infrared (SWIR) wavelength regions, offers a vast number of vegetation indices. Spectral indices, which are combinations of spectral measurements at different wavelengths, have been used in the previous studies and they sometimes contributed to improve classification accuracies. In this study, 91 published spectral indices were calculated from the MSI data. Additionally, classification algorithms are essential for generating accurate maps and the random forests classifier is one of which possesses the five hyperparameters were applied. The improvements in classification accuracies were confirmed achieving an overall accuracy of 93.1% based on the reflectance at 4 bands and 8 spectral indices.

Continuous monitoring of blood pressure, an essential measure of health status, typically requires complex, costly, and invasive techniques that can expose patients to risks of complications. Continuous, cuffless, and noninvasive blood pressure monitoring methods that correlate measured pulse wave velocity (PWV) to the blood pressure via the Moens–Korteweg (MK) and Hughes Equations, offer promising alternatives. The MK Equation, however, involves two assumptions that do not hold for human arteries, and the Hughes Equation is empirical, without any theoretical basis. The results presented here establish a relation between the blood pressure P and PWV that does not rely on the Hughes Equation nor on the assumptions used in the MK Equation. This relation degenerates to the MK Equation under extremely low blood pressures, and it accurately captures the results of in vitro experiments using artificial blood vessels at comparatively high pressures. For human arteries, which are well characterized by the Fung hyperelastic model, a simple formula between P and PWV is established within the range of human blood pressures. This formula is validated by literature data as well as by experiments on human subjects, with applicability in the determination of blood pressure from PWV in continuous, cuffless, and noninvasive blood pressure monitoring systems.

As sweat biomarker levels are continuously changing over metabolism and daily activities, pathological and physiological processes can be dynamically analyzed by wearable devices. The colorimetric skin-interfaced microfluidic devices that do not have external circuit modules exhibit enhanced deformability with a small footprint. However, it is difficult to achieve sampling over time and self-feedback for closed-loop systems. This review summarizes recent advances in microfluidic valves for biofluid management and chrono-sampling, as well as active triggers in microfluidics self-feedback. After enumerating the current limitations in temporal resolution and reliability, we further point out a few potential feasible strategies for future developments.

Melatonin (MT) plays integral roles in regulating several biological processes including plant growth, seed germination, flowering, senescence, and stress responses. This study investigated the effects of MT on adventitious root formation (ARF) of de-rooted tomato seedlings. Exogenous MT positively or negatively influenced ARF, which was dependent on the concentration of MT application. In the present experiment, 50 μM MT showed the best effect on inducing ARF. Interestingly, exogenous MT promoted the accumulation of endogenous nitric oxide (NO) by down-regulating the expression of S-nitrosoglutathione reductase (GSNOR). To determine the interaction of MT and NO in ARF, MT synthesis inhibitor p-chlorophenylalanine, NO scavenger 2-(4-carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide potassium salt as well as GSNOR-overexpression plants with low NO levels were used. The function of MT was removed by NO scavenger or GSNOR-overexpression plants. However, application of MT synthesis inhibitor did little to abolish the function of NO. These results indicate that NO, as a downstream signal, was involved in the MT-induced ARF. Concentrations of indole-3-acetic acid and indole-3-butyric acid, as well as the expression of several genes related to the auxin signaling pathway (PIN1, PIN3, PIN7, IAA19, and IAA24), showed that MT influenced auxin transport and signal transduction as well as auxin accumulation through the NO signaling pathway. Collectively, these strongly suggest that elevated NO levels resulting from inhibited GSNOR activity and auxin signaling were involved in the MT-induced ARF in tomato plants. This can be applied in basic research and breeding.

Epilepsy is one of the most common neurological disorders characterized by recurrent seizures. The mechanism of epilepsy remains unclear and previous studies suggest that N-methyl-D-aspartate receptors (NMDARs) play an important role in abnormal discharges, nerve conduction, neuron injury and inflammation, thereby they may participate in epileptogenesis. NMDARs belong to a family of ionotropic glutamate receptors that play essential roles in excitatory neurotransmission and synaptic plasticity in the mammalian CNS. Despite numerous studies focusing on the role of NMDAR in epilepsy, the relationship appeared to be elusive. In this article, we reviewed the regulation of NMDAR and possible mechanisms of NMDAR in epilepsy and in respect of onset, development, and treatment, trying to provide more evidence for future studies.

Cancer-associated fibroblasts (CAFs) are pivotal in shaping the immunosuppressive and chemoresistant tumor microenvironment (TME) of osteosarcoma (OS). This review explores how CAFs drive OS progression through paracrine signaling (e.g., TGF-β, IL-6), extracellular matrix (ECM) remodeling, exosome-mediated crosstalk, and metabolic reprogramming. We highlight CAF heterogeneity (e.g., myCAFs, iCAFs) and their roles in therapy resistance, emphasizing emerging strategies such as FAP inhibitors, TGF-β blockers, and CXCR4 antagonists. Combining these approaches with immunotherapy or chemotherapy offers promise for overcoming chemoresistance. Challenges like CAF plasticity and biomarker development are discussed, alongside future directions for precision targeting in OS.

Type 2 diabetes mellitus (T2DM) is a metabolic disease accompanied by a series of diseases such as diabetic nephropathy. The drug pair (HS) of Astragalus Radix (HQ) and Dioscoreae Rhizoma (SY) was designed by Dr. Shi Jinmo to improve the treatment of T2DM. However, the exact mechanism involved requires further clarification. In this work, 1H-NMR–based metabonomics and network pharmacology were adopted. Metabolic profiling indicated that the metabolic perturbation was reduced after HS treatment. The results found 21 biomarkers. According to the network pharmacology, we found that the regulation of T2DM was primarily associated with 18 active compounds in HS. These active compounds mainly had an effect on 135 targets. Subsequently, combining network pharmacology and metabonomics, we found four target proteins, which indicated that HS has potential hypoglycemic effects through regulating monoamine oxidases B (MAOB), acetyl-CoA carboxylase 1 (ACACA), carbonic anhydrase 2 (CA2), and catalase (CAT). In conclusion, the result showed that these four targets might be the most relevant targets for the treatment of T2DM with HS. This study clarified the mechanism of HS in the treatment of T2DM and also confirmed the feasibility of combining metabonomics and network pharmacology to study the mechanisms of traditional Chinese medicine (TCM). In the future, this approach may be a potentially powerful tool to discovery active components of traditional Chinese medicines and elucidate their mechanisms.

Tislelizumab is an anti-programmed cell death protein 1(anti-PD-1) monoclonal antibody, which was approved by the Food and Drug Administration(FDA) on March 14, 2024. However, clinical studies are often limited by small sample sizes, and thus a more comprehensive evaluation of the safety of Tislelizumab, particularly its immune-related adverse reactions, is urgently needed. Disproportionality analysis was used in this study to assess the safety of Tislelizumab in clinical practice by analyzing all adverse event reports from the FDA Adverse Event Reporting System database, starting from the first quarter of 2024, where Tislelizumab was identified as the primary suspected drug. Two disproportionality analysis methods, reporting odds ratio (ROR) and Bayesian confidence propagation neural network (BCPNN), were utilized to investigate the adverse reactions related to Tislelizumab. Additionally, the Weibull distribution was employed to examine the time-dependent changes in the incidence of adverse events. Consistent with the drug label, this study identified significant positive signals for adverse reactions, including myelosuppression, hepatic dysfunction, pruritus, rash, and exfoliative dermatitis. Notably, this study also identified several adverse reactions not documented in the drug label, including palmar-plantar erythrodysaesthesia syndrome, immune-mediated cystitis, and renal cysts. Adverse reactions associated with Tislelizumab generally manifested within the first month of treatment. In terms of immune-related adverse reactions, Tislelizumab demonstrated lower signal values compared to other immune checkpoint inhibitors. This study comprehensively reviews the safety profile of Tislelizumab, thereby providing clinicians with crucial safety information for prescribing this drug. Due to its relatively low risk of immune-related adverse events (irAEs), Tislelizumab may serve as a promising candidate for combination therapy with other immune checkpoint inhibitors (ICIs). Novel combination strategies involving Tislelizumab and other ICIs are anticipated to provide new therapeutic opportunities for patients experiencing irAEs.

Gold nanoclusters (AuNCs) have been widely investigated because of their unique photoluminescence properties. However, the applications of AuNCs are limited by their poor stability and relatively low fluorescence. In the present work, we developed nanocomposites (L-Cys-AuNCs@ZIF-8) with high fluorescence and stability, which were constructed by encapsulating the water-dispersible L-Cys-AuNCs into a ZIF-8 via Zn2+-triggered growth strategy without high temperature and pressure. The maximum emission wavelength of the L-Cys-AuNCs@ZIF-8 composite was at 868 nm, and the fluorescence intensity of L-Cys-AuNCs@ZIF-8 was nearly nine-fold compared with L-Cys-AuNCs without the ZIF-8 package. The mechanism investigation by fluorescence spectroscopy and X-ray photoelectron spectroscopy showed that L-Cys-AuNCs@ZIF-8 impeded ligand rotation, induced energy dissipation, and diminished the self-quenching effect, attributing to the spatial distribution of L-Cys-AuNCs. Based on the high fluorescence efficiency of L-Cys-AuNCs@ZIF-8, a “signal off” detective platform was proposed with copper ions as a model analyte, achieving a sensitive detection limit of Cu2+ at 16.7 nM. The quenching mechanism was confirmed, showing that the structure of the L-Cys-AuNCs@ZIF-8 nanocomposites was collapsed by the addition of Cu2+. Attributing to the strong adsorption ability between copper ions and pyridyl nitrogen, the as-prepared L-Cys-AuNCs@ZIF-8 was shown to accumulate Cu2+, and the Zn2+ in ZIF-8 was replaced by Cu2+.