Alternating current stimulation promotes neurite outgrowth and plasticity in neurons through activation of the PI3K/AKT signaling pathway.

As a commonly used physical intervention, electrical stimulation (ES) has been demonstrated to be effective in the treatment of central nervous system disorders. Currently, researchers are studying the effects of electrical stimulation on individual neurons and neural networks, which are dependent on factors such as stimulation intensity, duration, location, and neuronal properties. However, the exact mechanism of action of electrical stimulation remains unclear. In some cases, repeated or prolonged electrical stimulation can lead to changes in the morphology or function of the neuron. In this study, immunofluorescence staining and Sholl analysis are used to assess changes in the neurite number and axon length to determine the optimal pattern and stimulation parameters of ES for neurons. Neuronal death and plasticity are detected by TUNEL staining and microelectrode array assays, respectively. mRNA sequencing and bioinformatics analysis are applied to predict the key targets of the action of ES on neurons, and the identified targets are validated by western blot analysis and qRT-PCR. The effects of alternating current stimulation (ACS) on neurons are more significant than those of direct current stimulation (DCS), and the optimal parameters are 3 µA and 20 min. ACS stimulation significantly increases the number of neurites, the length of axons and the spontaneous electrical activity of neurons, significantly elevates the expression of growth-associated protein-43 (GAP-43) without significant changes in the expression of neurotrophic factors. Furthermore, application of PI3K/AKT-specific inhibitors significantly abolishes the beneficial effects of ACS on neurons, confirming that the PI3K/AKT pathway is an important potential signaling pathway in the action of ACS.

A potential therapeutic approach for ulcerative colitis: targeted regulation of macrophage polarization through phytochemicals.

Ulcerative colitis (UC), a type of inflammatory bowel disease characterized by recurring and incurable symptoms, causes immense suffering and economic burden for patients due to the limited treatment options available. Therefore, it is imperative to develop novel and promising strategies, as well as safe and effective drugs, for the clinical management of UC. Macrophages play a critical role as the initial line of defense in maintaining intestinal immune homeostasis, and their phenotypic transformation significantly influences the progression of UC. Scientific studies have demonstrated that directing macrophage polarization toward the M2 phenotype is an effective strategy for the prevention and treatment of UC. Phytochemicals derived from botanical sources have garnered the interest of the scientific community owing to their distinct bioactivity and nutritional value, which have been shown to confer beneficial protective effects against colonic inflammation. In this review, we explicated the influence of macrophage polarization on the development of UC and collated data on the significant potential of natural substances that can target the macrophage phenotype and elucidate the possible mechanism of action for its treatment. These findings may provide novel directions and references for the clinical management of UC.

The application of machine learning algorithms in predicting the length of stay following femoral neck fracture.

PURPOSE: Femoral neck fracture is a frequent cause of hospitalization, and length of stay is an important marker of hospital cost and quality of care provided. As an extension of traditional statistical methods, machine learning provides the possibility of accurately predicting the length of hospital stay. The aim of this paper is to retrospectively identify predictive factors of the length of hospital stay (LOS) and predict the postoperative LOS by using machine learning algorithms. METHOD: Based on the admission and perioperative data of the patients, linear regression was used to analyze the predictive factors of the LOS. Multiple machine learning models were developed, and the performance of different models was compared. RESULT: Stepwise linear regression showed that preoperative calcium level (P = 0.017) and preoperative lymphocyte percentage (P = 0.007), in addition to intraoperative bleeding (p = 0.041), glucose and sodium chloride infusion after surgery (P = 0.019), Charlson Comorbidity Index (p = 0.007) and BMI (P = 0.031), were significant predictors of LOS. The best performing model was the principal component regression (PCR) with an optimal MAE (1.525) and a proportion of prediction error within 3 days of 90.91%. CONCLUSION: Excessive intravenous glucose and sodium chloride infusion after surgery, preoperative hypocalcemia, preoperative high percentages of lymphocytes, excessive intraoperative bleeding, lower BMI and higher CCI scores were related to prolonged LOS by using linear regression. Machine learning could accurately predict the postoperative LOS. This information allows hospital administrators to plan reasonable resource allocation to fulfill demand, leading to direct care quality improvement and more reasonable use of scarce resources.

Identification of fishy odor causing compounds produced by Ochromonas sp. and Cryptomonas ovate with gas chromatography-olfactometry and comprehensive two-dimensional gas chromatography.

Disgusting fishy odor problems have become a major concern in drinking water quality, and are commonly related to algal proliferation in source water. Unlike the typical musty/earthy odorants 2-methylisoborneol (MIB) and geosmin, identification of the corresponding fishy odorants is still a big challenge. In this study, two species of fishy-odor-producing algae, Ochromonas sp. and Cryptomonas ovate, were cultured to explore the odor production characteristics and typical odorants. When algae were ruptured in the stationary and decline phases, fishy odor intensities of 4 to 8 characterized by FPA were produced. However, some frequently reported aldehydes that could cause fishy odor, including n-hexanal, 2-octenal, heptanal, 2,4-heptanal and 2,4-decadienal, were not detected in either of the cultured algae. The possible fishy odor-causing compounds were further identified by combining gas chromatography-olfactometry (GC-O/MS) and comprehensive two-dimensional gas chromatography (GC × GC-TOFMS) using retention indices (RIs). From GC-O/MS analysis, twelve and six olfactometry peaks with various odor characteristics were identified in Ochromonas sp. and Cryptomonas ovate, respectively, of which three and two olfactometry peaks showed fishy odor characteristics. 2-Nonenal, 2,4-octadienal, fluorene and 2-tetradecanone were identified as fishy odorants in Ochromonas sp., and 1-octen-3-ol, 6-methyl-5-hepten-2-one, 1-octen-3-one, 2-nonenal and 2,4-octadienal were identified in Cryptomonas ovate. Other identified compounds, including butyl butanoate (fragrant odor), ionone (fragrant odor), bis (2-chloroisopropyl) ether (chemical odor) etc., did not show fishy features. Therefore, the fishy odor might be a synthetic and comprehensive odor, which resulted from the combination of different odorants and their synergistic effects. The results of this study will be helpful for understanding fishy odor problems, which will provide support for fishy odor management and control in the drinking water industry.