A family of dual-anion-based sodium superionic conductors for all-solid-state sodium-ion batteries.

The sodium (Na) superionic conductor is a key component that could revolutionize the energy density and safety of conventional Na-ion batteries. However, existing Na superionic conductors are primarily based on a single-anion framework, each presenting inherent advantages and disadvantages. Here we introduce a family of amorphous Na-ion conductors (Na2O2-MCly, M = Hf, Zr and Ta) based on the dual-anion framework of oxychloride. Benefiting from a dual-anion chemistry and with the resulting distinctive structures, Na2O2-MCly electrolytes exhibit room-temperature ionic conductivities up to 2.0 mS cm-1, wide electrochemical stability windows and desirable mechanical properties. All-solid-state Na-ion batteries incorporating amorphous Na2O2-HfCl4 electrolyte and a Na0.85Mn0.5Ni0.4Fe0.1O2 cathode exhibit a superior rate capability and long-term cycle stability, with 78% capacity retention after 700 cycles under 0.2 C (1C = 120 mA g-1) at room temperature. The discoveries in this work could trigger a new wave of enthusiasm for exploring new superionic conductors beyond those based on a single-anion framework.

Bimetallic Cu11Ag3 Nanotips for Ultrahigh Yield Rate of Nitrate-to-Ammonium.

Electrochemical reduction of nitrate to ammonia (NRA) offers a sustainable approach for NH3 production and NO3- removal but suffers from low NH3 yield rate (<1.20 mmol h-1 cm-2). We present bimetallic Cu11Ag3 nanotips with tailored local environment and tip-enhanced effects, which achieve an ultrahigh NH3 yield rate of 2.36 mmol h-1 cm-2 at a low applied potential of -0.33 V vs. RHE, a high Faradic efficiency (FE) of 98.8%, and long-term operation stability at 1800 mg-N L-1 NO3-, outperforming most of the recently reported catalysts. At a NO3- concentration as low as 15 mg-N L-1, it still delivers a high FE of 86.9% and an NH3 selectivity of 93.8%. Operando ATR-FTIR spectra, finite-element method, and DFT calculations reveal that the Cu11Ag3 exhibits reduced adsorption energy barrier of *N intermediates, favorable water dissociation for *H generation and high energy barrier for H2 formation, while its tip-enhanced enrichment promoting NO3- accumulation.

Prenatal glucocorticoids exposure and adverse cardiovascular effects in offspring.

Glucocorticoids (GCs) are steroid hormones fundamental to the body's normal physiological functions and are pivotal in fetal growth and development. During gestation, the mother's cortisol concentration (active GCs) escalates to accommodate the requirements of fetal organ development and maturation. A natural placental GCs barrier, primarily facilitated by 11ß hydroxysteroid dehydrogenase 2, exists between the mother and fetus. This enzyme transforms biologically active cortisol into biologically inactive corticosterone, thereby mitigating fetal GCs exposure. However, during pregnancy, the mother may be vulnerable to adverse factor exposures such as stress, hypoxia, caffeine, and synthetic GCs use. In these instances, maternal serum GCs levels may surge beyond the protective capacity of the placental GCs barrier. Moreover, these adverse factors could directly compromise the placental GCs barrier, resulting in excessive fetal exposure to GCs. It is well-documented that prenatal GCs exposure can detrimentally impact the offspring's cardiovascular system, particularly in relation to blood pressure, vascular function, and heart function. In this review, we succinctly delineate the alterations in GCs levels during pregnancy and the potential mechanisms driving these changes, and also analyze the possible causes of prenatal GCs exposure. Furthermore, we summarize the current advancements in understanding the adverse effects and mechanisms of prenatal GCs exposure on the offspring's cardiovascular system.

Screening of Silver-Based Single-Atom Alloy Catalysts for NO Electroreduction to NH3 by DFT Calculations and Machine Learning.

Exploring NO reduction reaction (NORR) electrocatalysts with high activity and selectivity toward NH3 is essential for both NO removal and NH3 synthesis. Due to their superior electrocatalytic activities, single-atom alloy (SAA) catalysts have attracted considerable attention. However, the exploration of SAAs is hindered by a lack of fast yet reliable prediction of catalytic performance. To address this problem, we comprehensively screened a series of transition-metal atom doped Ag-based SAAs. This screening process involves regression machine learning (ML) algorithms and a compressed-sensing data-analytics approach parameterized with density-functional inputs. The results demonstrate that Cu/Ag and Zn/Ag can efficiently activate and hydrogenate NO with small Φmax(η), a grand-canonical adaptation of the Gmax(η) descriptor, and exhibit higher affinity to NO over H adatoms to suppress the competing hydrogen evolution reaction. The NH3 selectivity is mainly determined by the s orbitals of the doped single-atom near the Fermi level. The catalytic activity of SAAs is highly correlated with the local environment of the active site. We further quantified the relationship between the intrinsic features of these active sites and Φmax(η). Our work clarifies the mechanism of NORR to NH3 and offers a design principle to guide the screen of highly active SAA catalysts.

Effects of aripiprazole on prolactin levels and differences in effectiveness in patients with schizophrenia: a post-hoc analysis of the real-world data of a multicenter study.

Objectives: To investigate the effect of aripiprazole on prolactin levels in patients with schizophrenia and analyze whether varying baseline prolactin levels affect the effectiveness and safety of aripiprazole, in a real-life diagnostic and therapeutic setting in a post-hoc analysis. Methods: In this post-hoc analysis, patients with schizophrenia in the acute phase were divided into an elevated-prolactin group and a normal-prolactin group. After 8 weeks of aripiprazole treatment, changes in the proportion of patients with an abnormal prolactin level were analyzed in both groups, and the efficacy and safety of aripiprazole were compared between the two groups. Results: The elevated-prolactin group had more women, a longer duration of disease, and lower Positive and Negative Syndrome Scale (PANSS) total and subscale scores at baseline compared with the normal-prolactin group (all P < 0.05), and there was no significant difference in the proportion of patients with prior use of antipsychotic medication between the two groups. Regardless of previous antipsychotic use, patients in both groups developed hyperprolactinemia (23/168 [13.7%] in those who had taken antipsychotics vs. 43/375 [11.4%] in those who had not). After 8 weeks of aripiprazole treatment, the proportion of patients with abnormal prolactin in the elevated-prolactin group significantly decreased with prolonged treatment (P < 0.001), and aripiprazole had no significant effect on the normal-prolactin group (P = 0.250). Normal-prolactin group showed better efficacy than the elevated-prolactin group, and the differences in efficacy between the two groups was observed from week 4 to the endpoint (all p<0.05). In total, 87.2% (68/78) patients experienced mild to moderate adverse events in the elevated-prolactin group, which was significantly more frequent compared with the normal-prolactin group 71.0% (365/514). Conclusions: In this real-world study, for patients with acute schizophrenia, aripiprazole was effective in lowering the proportion of patients with abnormal prolactin levels, while it had no significant effect on patients with normal baseline prolactin. After adjusting for factors such as sex, age, prior antipsychotic drugs use history and disease severity, effectiveness and safety of aripiprazole in patients with normal baseline prolactin was significantly better than that in patients with elevated baseline prolactin.

Effects and mechanisms of harmine on ameliorating ethanol-induced memory impairment.

ETHNOPHARMACOLOGICAL RELEVANCE: Peganum harmala L., a traditional Uyghur ethnic medicine widely used in China, is commonly used in the treatment of conditions such as hemiplegia, forgetfulness, cough, and asthma. Harmine and other ß-carboline alkaloids, one of the main active ingredients in P. harmala, have exhibited various pharmacological activities, including anti-Alzheimer's, antidepressant, anti-inflammatory, and antioxidant effects. However, the effects and underlying mechanisms of harmine on improving ethanol-induced memory impairment remain unclear. AIM OF THE STUDY: This study aimed to investigate the effects of harmine on ameliorating ethanol-induced memory impairment, and to explore potential mechanisms. MATERIALS AND METHODS: Ethanol (30%, i. g.) was used to induce memory impairment model. The effect of harmine on memory impairment was evaluated by Morris water maze (MWM). The histopathological analysis, immunofluorescence staining, RT-qPCR and UHPLC-MS/MS methods were performed to further investigate the underlying mechanisms. RESULTS: MWM experiments showed that harmine significantly improved ethanol-induced spatial learning memory deficit. Harmine exhibited anti-inflammatory effect by downregulating inflammatory factors such as IL-6, IL-1ß and tumor necrosis factor-α (TNF-α) induced by ethanol. Harmine also upregulated brain-derived neurotrophic factor (BDNF) levels to exert neuroprotective effect. Moreover, harmine protected neuronal cells and increased the protein expression of myelin basic protein (MBP). The cellular results indicated that harmine protected SH-SY5Y cells from ethanol-induced cytotoxicity and upregulated the relative mRNA expression of synaptosome associated protein 25 (SNAP25), syntaxin 1 A (STX1A), vesicle associated membrane protein 2 (VAMP2), synaptotagmin 1 (SYT1) and synaptophysin (SYP). CONCLUSIONS: Harmine improved ethanol-induced memory impairment by ameliorating inflammation, increasing BDNF levels, promoting synaptic vesicle fusion, protecting myelin sheath, and modulating neurotransmitter levels. These findings provided a scientific basis for development of therapeutic drugs for alcohol-induced memory impairments and other related disorders.

Metabolic profiling and pharmacokinetic studies of alkamides, a pair of cis-trans isomers N-isobutyl-2E,4E,8Z,10E/Z-dodecatetraenamide, from Asari Radix et Rhizoma by UHPLC-Q/TOF-MS and UHPLC-MS/MS.

Cis-trans isomers of N-isobutyl-2E,4E,8Z,10E-dodecatetraenamide (DDA-E) and N-isobutyl-2E,4E,8Z,10Z-dodecatetraenamide (DDA-Z) are representative alkamides with numbness of tongue, anti-inflammatory and analgesic activities of Asari Radix et Rhizoma. However, their respective metabolic pathways and pharmacokinetic behaviors are still unknown. This study aim to investigate the metabolism of the two alkamides in vitro and in vivo using ultra-high-performance liquid chromatography-quadruple-time-of-flight mass spectrometry. Furthermore, a rapid, sensitive, and selective ultra-high-performance liquid chromatography-tandem mass spectrometry method was developed to quantify DDA-E/Z in rat plasma. Results indicated that DDA-E and DDA-Z showed significant differences in metabolism and pharmacokinetics. Across all samples, 24 metabolites of DDA-E and 21 metabolites of DDA-Z were detected. A variety of pathways were involved in the production of these metabolites, mainly hydroxylation and oxidation. The linear range of DDA-E/Z was 1-2500 ng/mL (R2 = 0.9984), and the lowest quantification limit was 1 ng/mL. Precision, accuracy, extraction recovery, matrix effect, and stability of DDA-E/Z were within acceptable limits. Pharmacokinetic research was conducted using male Sprague-Dawley rats receiving intravenous (1 mg/kg) or intragastric (40 mg/kg) administration of DDA-E or DDA-Z solution. There was a calculated absolute bioavailability of 15.67 % for DDA-E and 4.83 % for DDA-Z when consumed orally. The apparent volume of distribution of intravenous and intragastric administrations were 4.44 ± 0.41 L/kg and 5.18 ± 0.67 L/kg for DDA-E, and 1.56 ± 1.66 L/kg and 2.35 ± 0.42 L/kg for DDA-Z. The maximal plasma concentrations of DDA-E and DDA-Z were 599.84 ± 149.92 nM and 422.09 ± 69.17 nM, and the time to maximum peak were 4.33 ± 3.51 h and 0.70 ± 1.12 h, respectively. In conclusion, in subsequent pharmacodynamics and safety evaluation studies, great attention should be paid to the metabolic characteristics and pharmacokinetic differences between DDA-E and DDA-Z.

Promoting Electrochemical Nitrate Reduction to Ammonia on Silver Nanocrystals Doped with Iron Series Elements.

Electrochemical nitrate reduction to ammonia (NRA) is a promising approach to remove environmental pollutants while producing green NH3 under ambient conditions. Ag-based nanomaterials have been used in NRA but their iron series elements (Fe, Co, Ni) doping has not been explored yet. Herein, an effective and versatile doping strategy of Ag nanocrystals by iron series elements for efficient NRA is presented. Experimental results show that doping with Fe, Co or Ni can improve the NRA activity. Among the catalysts, AgCo delivers the best performance with a Faraday efficiency (FE) of 88.3 % and ammonia selectivity of 97.4 % at-0.23 V vs RHE, which is 1.9 and 6.2 times higher than that of plain Ag (46.4 % FE and 15.8 % selectivity), respectively. A highest NO3 - conversion rate of AgCo (91.8 %) is achieved, which maintains 16.4 ppm NO3 --N in 4 hours, meeting the drinking water level (~15 ppm NO3 --N). Moreover, the FE, selectivity, conversion rate of AgCo do not decay after the four consecutive cycles. It is found that Co doping can effectively induce the change of Ag d-band center for optimized NRA. This work reveals doping effects of iron series elements on Ag-based catalysts, and shows potential practical application in NRA.

Characterization of active alkaloids and metabolites in rats after oral administration of Zuojin Pill using UHPLC-Q-TOF-MS combined with bioinformatics and molecular docking analyses.

Zuojin Pill (ZJP), a traditional Chinese medicine prescription composed of Rhizoma Coptidis and Euodiae Fructus in the ratio of 6:1 (w/w), has been widely used for the treatment of gastric disorders. However, an in-depth understanding of in vivo metabolism and distribution profiles of protoberberine alkaloids (PBAs) and indole alkaloids (IDAs) in ZJP is lacking. In this study, a method using ultra-high performance liquid chromatography coupled with quadruple time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) was developed to systematically screen the alkaloids and their metabolites in rat plasma and various tissues after oral administration of ZJP. Furthermore, bioinformatics and molecular docking analyses were conducted to elucidate the contribution of the alkaloids and metabolites enriched in the stomach to the therapeutic effect of ZJP on gastritis. A total of 33 compounds, including 7 prototype alkaloids and 26 metabolites, were chemically defined or tentatively identified in this work. The metabolic pathways of PBAs (hydroxylation, oxidation, reduction, demethylation, demethylenation, glucuronide conjugation, sulfate conjugation) and IDAs (hydroxylation, glucuronide conjugation) were revealed. Notably, 7 prototype alkaloids and 18 metabolites were detected in the stomach, indicating their propensity for gastric distribution. These alkaloids and metabolites showed strong affinities with the 7 hub targets associated with gastritis, such as CCR7, CXCR4, IL6, IFNG, CCL2, TNF, and PTPRC, and could be considered the potential active substances of ZJP for treating gastritis. In conclusion, this study clarified the gastric distribution propensity of PBAs and IDAs and their metabolites, as well as their favorable binding interactions with gastritis-related targets, which could provide essential data for the further study of the pharmacodynamic material basis and gastroprotective mechanism of ZJP.

Discriminative analysis of schizophrenia and major depressive disorder using fNIRS.

BACKGROUND: Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions. METHODS: A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists. RESULTS: Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively. CONCLUSION: This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems.

Fall prediction, control, and recovery of quadruped robots.

When legged robots perform complex tasks in unstructured environments, falls are inevitable due to unknown external disturbances. However, current research mainly focuses on the locomotion control of legged robots without falling. This paper proposes a comprehensive decision-making and control framework to address the falling over of quadruped robots. First, a capturability-based fall prediction algorithm is derived for planar single-contact and 3D multi-contact locomotion with a predefined gait sequence. For safe fall control, a novel contact-implicit trajectory optimization method is proposed to generate both state and input trajectories and contact mode sequences. Specifically, incorporating uncertainty into the system and terrain models enables mitigating the non-smoothness of contact dynamics while improving the robustness of the resulting trajectories. Furthermore, a model-free deep reinforcement learning-based approach is presented to achieve fall recovery after the robot completes a fall. Experimental results demonstrate that the proposed fall prediction algorithm accurately predicts robot falls with up to 95% accuracy approximately 395ms in advance. Compared to classical locomotion controllers, which often struggle to maintain balance under significant pushes or terrain perturbations, the presented framework can autonomously switch to the fall controller approximately 0.06s after the perturbation, effectively preventing falls or achieving recovery with a threefold reduction in touchdown impact velocity. These findings highlight the effectiveness of the proposed framework in enhancing the stability and safety of legged robots in unstructured environments.

Predefined-Time Output-Feedback Leader-Following Consensus of Pure-Feedback Multiagent Systems.

This article delves into the predefined-time output-feedback leader-following consensus problem of uncertain pure-feedback nonlinear multiagent systems for the first time. To streamline subsequent design, the original systems in pure-feedback form are first transformed into canonical systems. Following this, a distributed predefined-time extended state observer (ESO) and a local predefined-time ESO are developed to reconstruct the unknown states/lumped disturbance of the transformed leader system and follower systems, respectively. Based on the estimated states and utilizing a bounded regulation function, two nonsingular and nonconservative predefined-time control laws are formulated to achieve consensus tracking. The proposed method showcases the following advantages: 1) the actual convergence time rather than the upper bound of the convergence time (UBCT) of the tracking errors can be explicitly specified a priori regardless of the initial conditions in a bounded region, optimizing control energy usage and 2) the system overshoot could be effectively reduced by selecting appropriate parameters for the regulation function. Finally, numerical examples are conducted to verify the obtained results.

Reviving Cost-Effective Organic Cathodes in Halide-Based All-Solid-State Lithium Batteries.

The evolution of inorganic solid electrolytes has revolutionized the field of sustainable organic cathode materials, particularly by addressing the dissolution problems in traditional liquid electrolytes. However, current sulfide-based all-solid-state lithium-organic batteries still face challenges such as high working temperatures, high costs, and low voltages. Here, we design an all-solid-state lithium battery based on a cost-effective organic cathode material phenanthrenequinone (PQ) and a halide solid electrolyte Li2ZrCl6. Thanks to the good compatibility between PQ and Li2ZrCl6, the PQ cathode achieved a high specific capacity of 248 mAh g-1 (96 % of the theoretical capacity), a high average discharge voltage of 2.74 V (vs. Li+/Li), and a good capacity retention of 95 % after 100 cycles at room temperature (25 °C). Furthermore, the interactions between the high-voltage carbonyl PQ cathode and both sulfide and halide solid electrolytes, as well as the redox mechanism of the PQ cathode in all-solid-state batteries, were carefully studied by a variety of advanced characterizations. We believe such a design and the corresponding investigations into the underlying chemistry give insights for the further development of practical all-solid-state lithium-organic batteries.

Lithium Metal-Compatible Antifluorite Electrolytes for Solid-State Batteries.

Lithium (Li) metal solid-state batteries feature high energy density and improved safety and thus are recognized as promising alternatives to traditional Li-ion batteries. In practice, using Li metal anodes remains challenging because of the lack of a superionic solid electrolyte that has good stability against reduction decomposition at the anode side. Here, we propose a new electrolyte design with an antistructure (compared to conventional inorganic structures) to achieve intrinsic thermodynamic stability with a Li metal anode. Li-rich antifluorite solid electrolytes are designed and synthesized, which give a high ionic conductivity of 2.1 × 10-4 S cm-1 at room temperature with three-dimensional fast Li-ion transport pathways and demonstrate high stability in Li-Li symmetric batteries. Reversible full cells with a Li metal anode and LiCoO2 cathode are also presented, showing the potential of Li-rich antifluorites as Li metal-compatible solid electrolytes for high-energy-density solid-state batteries.

Bridging the gap between academic research and industrial development in advanced all-solid-state lithium-sulfur batteries.

The energy storage and vehicle industries are heavily investing in advancing all-solid-state batteries to overcome critical limitations in existing liquid electrolyte-based lithium-ion batteries, specifically focusing on mitigating fire hazards and improving energy density. All-solid-state lithium-sulfur batteries (ASSLSBs), featuring earth-abundant sulfur cathodes, high-capacity metallic lithium anodes, and non-flammable solid electrolytes, hold significant promise. Despite these appealing advantages, persistent challenges like sluggish sulfur redox kinetics, lithium metal failure, solid electrolyte degradation, and manufacturing complexities hinder their practical use. To facilitate the transition of these technologies to an industrial scale, bridging the gap between fundamental scientific research and applied R&D activities is crucial. Our review will address the inherent challenges in cell chemistries within ASSLSBs, explore advanced characterization techniques, and delve into innovative cell structure designs. Furthermore, we will provide an overview of the recent trends in R&D and investment activities from both academia and industry. Building on the fundamental understandings and significant progress that has been made thus far, our objective is to motivate the battery community to advance ASSLSBs in a practical direction and propel the industrialized process.

Deciphering the critical role of interstitial volume in glassy sulfide superionic conductors.

Sulfide electrolytes represent a crucial category of superionic conductors for all-solid-state lithium metal batteries. Among sulfide electrolytes, glassy sulfide is highly promising due to its long-range disorder and grain-boundary-free nature. However, the lack of comprehension regarding glass formation chemistry has hindered their progress. Herein, we propose interstitial volume as the decisive factor influencing halogen dopant solubility within a glass matrix. We engineer a Li3PS4-Li4SiS4 complex structure within the sulfide glassy network to facilitate the release of interstitial volume. Consequently, we increase the dissolution capacity of LiI to 40 mol% in 75Li2S-25P2S5 glass. The synthesized glass exhibits one of the highest ionic conductivities among reported glass sulfides. Furthermore, we develop a glassy/crystalline composite electrolyte to mitigate the shortcomings of argyrodite-type sulfides by utilizing our synthesized glass as the filler. The composite electrolytes effectively mitigate Li intrusion. This work unveils a protocol for the dissolution of halogen dopants in glass electrolytes.

Recent tobacco smoking, restaurant and in-car secondhand smoke exposure are associated with depressive symptoms among young adults: a population-based cross-sectional analysis.

The purpose of this study was to evaluate the association between recent tobacco smoking, household secondhand smoke exposure, confined space secondhand smoke exposure and depressive symptoms in young adults after adjustments for each other. Data from NHANES 2013-2018 were extracted. A total of 4129 young adults age 18-35 years (mean age 26.11 ± 5.39 years, 2021 males and 2108 females) were included. Depressive symptoms were screened by PHQ-9. Recent tobacco smoking was assessed through question "smoked tobacco in the last 5 days?". Household secondhand smoke exposure was assessed through question "living with a smoker who smoked inside the house?". Confined space secondhand smoke exposure was assessed by SSEQ. Binary logistic regression models were performed to analyze the associations. Significant association were observed in recent tobacco smoking (OR = 1.593, 95% CI 1.318-1.926) and confined space secondhand smoke exposure (OR = 1.399, 95% CI 1.185-1.651), but not in household secondhand smoke exposure (P = 0.108). Among the different settings of confined space secondhand smoke exposure, restaurant (OR = 1.732, 95% CI 1.120-2.678) and in-car (OR = 1.350, 95% CI 1.102-1.652) exposure were significantly associated with depressive symptom after after fully adjustments.

Association between long-term sedentary behavior and depressive symptoms in U.S. adults.

The study aimed to investigate the association between long-term sedentary behavior (LTSB) and depressive symptoms within a representative sample of the U.S. adult population. Data from NHANES 2017-2018 were used, encompassing information on demographics, depressive symptoms, physical activity (PA), and LTSB. Depressive symptoms were identified using the Patient Health Questionnaire (PHQ-9), with "depressive symptoms" defined as a PHQ-9 score of ≥ 5, and "moderate to severe depressive symptoms (MSDS)" defined as a PHQ-9 score of ≥ 10. PA and LTSB were assessed through the Global Physical Activity Questionnaire, where LTSB was interpreted as sedentary time ≥ 600 min. Restricted Cubic Spline (RCS) curves were utilized to observe potential nonlinear relationships. Binary Logistic regressions were conducted to analyze the associations. A total of 4728 participants (mean age 51.00 ± 17.49 years, 2310 males and 2418 females) were included in the study. Among these individuals, 1194 (25.25%) displayed depressive symptoms, with 417 (8.82%) exhibiting MSDS. RCS curves displayed increased risk of depressive symptoms with prolonged sedentary duration. Logistic regression models indicated significant associations between LTSB and depressive symptoms (OR 1.398, 95% CI 1.098-1.780), and LTSB and MSDS (OR 1.567, 95% CI 1.125-2.183), after adjusting for covariates. These findings suggest that LTSB may act as a potential risk factor for both depressive symptoms and MSDS in the studied population.

MKP1 may be involved in the occurrence of depression by regulating hippocampal autophagy in rats.

BACKGROUND: Mitogen-activated protein kinase (MAPK) phosphatase-1 (MKP1) is upregulated in the hippocampus of patients with depression, while pharmacological inhibition of hippocampal MKP1 can mitigate depression-like behaviors in rodents. In addition, MAPK signaling regulates autophagy, and antidepressants were recently shown to target autophagic signaling pathways. We speculated that MKP1 contributes to depression by enhancing hippocampal autophagy through dephosphorylation of the MAPK isoform ERK1/2. METHODS: We established a rat depression model by exposure to chronic unpredictable mild stress (CUMS), and then examined depression-like behaviors in the sucrose preference test (SPT) and forced swimming test (FST) as well as expression changes in hippocampal MKP1, ERK1/2, phosphorylated ERK1/2, and autophagy-related proteins LC3II by Western blotting and immunostaining. These same measurements were repeated in rats exposed to CUMS following hippocampal infusion of a MKP1-targeted shRNA. Finally, the effects of MKP1 expression level on autophagy we examined in rat GMI-R1 microglia. RESULTS: CUMS-exposed rats demonstrated anhedonia in the SPT and helplessness in the FST, two core depression-like behaviors. Expression levels of MKP1 and LC3II were upregulated in the hippocampus of CUMS rats, suggesting enhanced autophagy, while pERK/ERK was downregulated. Knockdown of hippocampal MKP1 mitigated depression-like behaviors, downregulated hippocampal LC3II expression, and upregulated hippocampal pERK/ERK. Similarly, MKP1 knockdown in GMI-R1 cells upregulated pERK/ERK and reduced the number of LC3II autophagosomes, while MKP1 overexpression had the opposite effects. CONCLUSION: Enhanced hippocampal autophagy via MKP1-mediated ERK dephosphorylation may contribute to the development of depression.

Energy-Efficient Sleep Apnea Detection Using a Hyperdimensional Computing Framework Based on Wearable Bracelet Photoplethysmography.

OBJECTIVE: Sleep apnea syndrome (SAS) is a common sleep disorder, which has been shown to be an important contributor to major neurocognitive and cardiovascular sequelae. Considering current diagnostic strategies are limited with bulky medical devices and high examination expenses, a large number of cases go undiagnosed. To enable large-scale screening for SAS, wearable photoplethysmography (PPG) technologies have been used as an early detection tool. However, existing algorithms are energy-intensive and require large amounts of memory resources, which are believed to be the major drawbacks for further promotion of wearable devices for SAS detection. METHODS: In this paper, an energy-efficient method of SAS detection based on hyperdimensional computing (HDC) is proposed. Inspired by the phenomenon of chunking in cognitive psychology as a memory mechanism for improving working memory efficiency, we proposed a one-dimensional block local binary pattern (1D-BlockLBP) encoding scheme combined with HDC to preserve dominant dynamical and temporal characteristics of pulse rate signals from wearable PPG devices. RESULTS: Our method achieved 70.17 % accuracy in sleep apnea segment detection, which is comparable with traditional machine learning methods. Additionally, our method achieves up to 67× lower memory footprint, 68× latency reduction, and 93× energy saving on the ARM Cortex-M4 processor. CONCLUSION: The simplicity of hypervector operations in HDC and the novel 1D-BlockLBP encoding effectively preserve pulse rate signal characteristics with high computational efficiency. SIGNIFICANCE: This work provides a scalable solution for long-term home-based monitoring of sleep apnea, enhancing the feasibility of consistent patient care.