Aberrant individual large-scale functional network connectivity and topology in chronic insomnia disorder with and without depression.

Insomnia is increasingly prevalent with significant associations with depression. Delineating specific neural circuits for chronic insomnia disorder (CID) with and without depressive symptoms is fundamental to develop precision diagnosis and treatment. In this study, we examine static, dynamic and network topology changes of individual large-scale functional network for CID with (CID-D) and without depression to reveal their specific neural underpinnings. Seventeen individual-specific functional brain networks are obtained using a regularized nonnegative matrix factorization technique. Disorders-shared and -specific differences in static and dynamic large-scale functional network connectivities within or between the cognitive control network, dorsal attention network, visual network, limbic network, and default mode network are found for CID and CID-D. Additionally, CID and CID-D groups showed compromised network topological architecture including reduced small-world properties, clustering coefficients and modularity indicating decreased network efficiency and impaired functional segregation. Moreover, the altered neuroimaging indices show significant associations with clinical manifestations and could serve as effective neuromarkers to distinguish among healthy controls, CID and CID-D. Taken together, these findings provide novel insights into the neural basis of CID and CID-D, which may facilitate developing new diagnostic and therapeutic approaches.

Molecular basis underlying default mode network functional abnormalities in postpartum depression with and without anxiety.

Although Postpartum depression (PPD) and PPD with anxiety (PPD-A) have been well characterized as functional disruptions within or between multiple brain systems, however, how to quantitatively delineate brain functional system irregularity and the molecular basis of functional abnormalities in PPD and PPD-A remains unclear. Here, brain sample entropy (SampEn), resting-state functional connectivity (RSFC), transcriptomic and neurotransmitter density data were used to investigate brain functional system irregularity, functional connectivity abnormalities and associated molecular basis for PPD and PPD-A. PPD-A exhibited higher SampEn in medial prefrontal cortex (MPFC) and posterior cingulate cortex (PPC) than healthy postnatal women (HPW) and PPD while PPD showed lower SampEn in PPC compared to HPW and PPD-A. The functional connectivity analysis with MPFC and PPC as seed areas revealed decreased functional couplings between PCC and paracentral lobule and between MPFC and angular gyrus in PPD compared to both PPD-A and HPW. Moreover, abnormal SampEn and functional connectivity were associated with estrogenic level and clinical symptoms load. Importantly, spatial association analyses between functional changes and transcriptome and neurotransmitter density maps revealed that these functional changes were primarily associated with synaptic signaling, neuron projection, neurotransmitter level regulation, amino acid metabolism, cyclic adenosine monophosphate (cAMP) signaling pathways, and neurotransmitters of 5-hydroxytryptamine (5-HT), norepinephrine, glutamate, dopamine and so on. These results reveal abnormal brain entropy and functional connectivities primarily in default mode network (DMN) and link these changes to transcriptome and neurotransmitters to establish the molecular basis for PPD and PPD-A for the first time. Our findings highlight the important role of DMN in neuropathology of PPD and PPD-A.

Changes in Mental Health and EEG Biomarkers of Undergraduates Under Different Patterns of Mindfulness.

The effects of short-term mindfulness are associated with the different patterns (autonomic, audio guided, or experienced and certified mindfulness instructor guided mindfulness). However, robust evidence for reported the impacts of different patterns of mindfulness on mental health and EEG biomarkers of undergraduates is currently lacking. Therefore, we aimed to test the hypotheses that mindfulness training for undergraduates would improve mental health, and increase alpha power over frontal region and theta power over midline region at the single electrode level. We also describe the distinction among frequency bands patterns in different sites of frontal and midline regions. 70 participants were enrolled and assigned to either 5-day mindfulness or a waiting list group. Subjective questionnaires measured mental health and other psychological indicators, and brain activity was recorded during various EEG tasks before and after the intervention. The 5-day mindfulness training improved trait mindfulness, especially observing (p = 0.001, d = 0.96) and nonreactivity (p = 0.03, d = 0.56), sleep quality (p = 0.001, d = 0.91), and social support (p = 0.001, d = 0.95) while not in affect. Meanwhile, the expected increase in the alpha power of frontal sites (p < 0.017, d > 0.84) at the single electrode level was confirmed by the current data rather than the theta. Interestingly, the alteration of low-beta power over the single electrode of the midline (p < 0.05, d > 0.71) was difference between groups. Short-term mindfulness improves practitioners' mental health, and the potentially electrophysiological biomarkers of mindfulness on neuron oscillations were alpha activity over frontal sites and low-beta activity over midline sites.

Molecular mechanisms underlying human spatial cognitive ability revealed with neurotransmitter and transcriptomic mapping.

Mental rotation, one of the cores of spatial cognitive abilities, is closely associated with spatial processing and general intelligence. Although the brain underpinnings of mental rotation have been reported, the cellular and molecular mechanisms remain unexplored. Here, we used magnetic resonance imaging, a whole-brain spatial distribution atlas of 19 neurotransmitter receptors, transcriptomic data from Allen Human Brain Atlas, and mental rotation performances of 356 healthy individuals to identify the genetic/molecular foundation of mental rotation. We found significant associations of mental rotation performance with gray matter volume and fractional amplitude of low-frequency fluctuations in primary visual cortex, fusiform gyrus, primary sensory-motor cortex, and default mode network. Gray matter volume and fractional amplitude of low-frequency fluctuations in these brain areas also exhibited significant sex differences. Importantly, spatial correlation analyses were conducted between the spatial patterns of gray matter volume or fractional amplitude of low-frequency fluctuations with mental rotation and the spatial distribution patterns of neurotransmitter receptors and transcriptomic data, and identified the related genes and neurotransmitter receptors associated with mental rotation. These identified genes are localized on the X chromosome and are mainly involved in trans-synaptic signaling, transmembrane transport, and hormone response. Our findings provide initial evidence for the neural and molecular mechanisms underlying spatial cognitive ability.

Correction: Self-limiting stoichiometry in SnSe thin films.

Correction for 'Self-limiting stoichiometry in SnSe thin films' by Jonathan R. Chin et al., Nanoscale, 2023, 15, 9973-9984, https://doi.org/10.1039/D3NR00645J.

Early-Life Factors and Multimorbidity Risk Later in Older Age: Evidence Based on CHARLS.

INTRODUCTION: Early-life factors were reported to exert influence on the health condition of individuals in the long-term. However, limited research explored the connection between early-life factors and multimorbidity in later years. METHODS: We utilized the data from the China Health and Retirement Longitudinal Study to assess this possible association in the present cross-sectional study. Multimorbidity was determined based on 14 common chronic diseases included in the study. Logistic regression was employed to examine the link between early-life factors and subsequent multimorbidity. RESULTS: Out of 7,578 participants who met the inclusion criteria for analysis, 3,765 (49.68%) were females. The mean age was 68.25 ± 6.70 years. Participants who rated their health during childhood as average (odds ratio [OR] 0.78, 95% confidence interval [CI] 0.63-0.96) or better [OR 0.72, 95% CI 0.57-0.91] were significantly less likely to experience multimorbidity in older life. By contrast, experiencing violence from two of the family members was significantly associated with future multimorbidity (OR [95% CI], 1.29 [1.04-1.60]). A superior family financial situation was also negatively associated with multimorbidity, with average (OR [95% CI], 0.72 [0.63-0.83]) and better off than average (OR [95% CI], 0.76 [0.62-0.93]). DISCUSSION: Individuals with poor health status, inferior family socioeconomic status, or experienced violence from family members in childhood were more likely to suffer from multimorbidity in later life. Enhanced social monitoring of potentially adverse conditions in youngsters and targeted interventions could help mitigate the progression of multimorbidity in later life.

A feasibility study for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials and other related visual parameters with machine learning algorithm.

BACKGROUND: To develop machine learning models for objectively evaluating visual acuity (VA) based on pattern-reversal visual evoked potentials (PRVEPs) and other related visual parameters. METHODS: Twenty-four volunteers were recruited and forty-eight eyes were divided into four groups of 1.0, 0.8, 0.6, and 0.4 (decimal vision). The relationship between VA, peak time, or amplitude of P100 recorded at 5.7°, 2.6°, 1°, 34', 15', and 7' check sizes were analyzed using repeated-measures analysis of variance. Correlations between VA and P100, contrast sensitivity (CS), refractive error, wavefront aberrations, and visual field were analyzed by rank correlation. Based on meaningful P100 peak time, P100 amplitude, and other related visual parameters, four machine learning algorithms and an ensemble classification algorithm were used to construct objective assessment models for VA. Receiver operating characteristic (ROC) curves were used to compare the efficacy of different models by repeated sampling comparisons and ten-fold cross-validation. RESULTS: The main effects of P100 peak time and amplitude between different VA and check sizes were statistically significant (all P < 0.05). Except amplitude at 2.6° and 5.7°, VA was negatively correlated with peak time and positively correlated with amplitude. The peak time initially shortened with increasing check size and gradually lengthened after the minimum value was reached at 1°. At the 1° check size, there were statistically significant differences when comparing the peak times between the vision groups with each other (all P < 0.05), and the amplitudes of the vision reduction groups were significantly lower than that of the 1.0 vision group (all P < 0.01). The correlations between peak time, amplitude, and visual acuity were all highest at 1° (rs = - 0.740, 0.438). VA positively correlated with CS and spherical equivalent (all P < 0.001). There was a negative correlation between VA and coma aberrations (P < 0.05). For different binarization classifications of VA, the classifier models with the best assessment efficacy all had the mean area under the ROC curves (AUC) above 0.95 for 500 replicate samples and above 0.84 for ten-fold cross-validation. CONCLUSIONS: Machine learning models established by meaning visual parameters related to visual acuity can assist in the objective evaluation of VA.

A voxel-based morphometry investigation of brain structure variations in late-life depression with insomnia.

Background: Late-life depression (LLD) is linked to various medical conditions and influenced by aging-related processes. Sleep disturbances and insomnia symptoms may be early indicators or risk factors for depression. Neuroimaging studies have attempted to understand the neural mechanisms underlying LLD, focusing on different brain networks. This study aims to further delineate discriminative brain structural profiles for LLD with insomnia using MRI. Methods: We analyzed 24 cases in the LLD with insomnia group, 26 cases in the LLD group, and 26 in the healthy control (HC) group. Patients were evaluated using the Hamilton Depression Rating Scale (HAMD-17), Hamilton Anxiety Rating Scale (HAMA), Mini-Mental State Examination (MMSE), and Pittsburgh Sleep Quality Index (PSQI). Structural MRI data were gathered and analyzed using voxel-based morphometry (VBM) to identify differences in gray matter volume (GMV) among the groups. Correlation analyses were conducted to explore the relationships between GMV and clinical characteristics. Results: Significant difference in sex distribution was observed across the groups (p = 0.029). However, no significant differences were detected in age and MMSE scores among the groups. LLD with insomnia group exhibited significantly higher HAMA (p = 0.041) and PSQI scores (p < 0.05) compared to the LLD group. ANOVA identified significant difference in GMV of anterior lobe of cerebellum (peak MNI coordinate: x = 52, y = -40, z = -30) among HC, LLD, and LLD with insomnia. Post-hoc two-sample t-tests revealed that the significant difference in GMV was only found between the LLD group and the HC group (p < 0.05). The mean GMV in the cerebellum was positively correlated with HAMA scale in LLD patients (r = 0.47, p < 0.05). Conclusion: There is significant difference in GMV in the LLD group, the association between late-life depression and insomnia may be linked to anxiety. This study provides insights into the discriminative brain structural profiles of LLD and LLD with insomnia, advancing the understanding of the underlying neural mechanisms and potential targets for intervention.

Self-limiting stoichiometry in SnSe thin films.

Unique functionalities can arise when 2D materials are scaled down near the monolayer limit. However, in 2D materials with strong van der Waals bonds between layers, such as SnSe, maintaining stoichiometry while limiting vertical growth is difficult. Here, we describe how self-limiting stoichiometry can promote the growth of SnSe thin films deposited by molecular beam epitaxy. The Pnma phase of SnSe was stabilized over a broad range of Sn : Se flux ratios from 1 : 1 to 1 : 5. Changing the flux ratio does not affect the film stoichiometry, but influences the predominant crystallographic orientation. ReaxFF molecular dynamics (MD) simulation demonstrates that, while a mixture of Sn/Se stoichiometries forms initially, SnSe stabilizes as the cluster size evolves. The MD results further show that the excess selenium coalesces into Se clusters that weakly interact with the surface of the SnSe particles, leading to the limited stoichiometric change. Raman spectroscopy corroborates this model showing the initial formation of SnSe2 transitioning into SnSe as experimental film growth progresses. Transmission electron microscopy measurements taken on films deposited with growth rates above 0.25 Å s-1 show a thin layer of SnSe2 that disrupts the crystallographic orientation of the SnSe films. Therefore, using the conditions for self-limiting SnSe growth while avoiding the formation of SnSe2 was found to increase the lateral scale of the SnSe layers. Overall, self-limiting stoichiometry provides a promising avenue for maintaining growth of large lateral-scale SnSe for device fabrication.

Mechanistic Investigations of Thermal Decomposition of Perfluoroalkyl Ether Carboxylic Acids and Short-Chain Perfluoroalkyl Carboxylic Acids.

In this study, we investigated the thermal decomposition mechanisms of perfluoroalkyl ether carboxylic acids (PFECAs) and short-chain perfluoroalkyl carboxylic acids (PFCAs) that have been manufactured as replacements for phased-out per- and polyfluoroalkyl substances (PFAS). C-C, C-F, C-O, O-H, and C═C bond dissociation energies were calculated at the M06-2X/Def2-TZVP level of theory. The α-C and carboxyl-C bond dissociation energy of PFECAs declines with increasing chain length and the attachment of an electron-withdrawing trifluoromethyl (-CF3) group to the α-C. Experimental and computational results show that the thermal transformation of hexafluoropropylene oxide dimer acid to trifluoroacetic acid (TFA) occurs due to the preferential cleavage of the C-O ether bond close to the carboxyl group. This pathway produces precursors of perfluoropropionic acid (PFPeA) and TFA and is supplemented by a minor pathway (CF3CF2CF2OCFCF3COOH → CF3CF2CF2· + ·OCFCF3COOH) through which perfluorobutanoic acid (PFBA) is formed. The weakest C-C bond in PFPeA and PFBA is the one connecting the α-C and the ß-C. The results support (1) the C-C scission in the perfluorinated backbone as an effective PFCA thermal decomposition mechanism and (2) the thermal recombination of radicals through which intermediates are formed. Additionally, we detected a few novel thermal decomposition products of studied PFAS.

Globular adiponectin ameliorates insulin resistance in skeletal muscle by enhancing the LKB1-mediated AMPK activation via SESN2.

Adiponectin has been demonstrated to be a mediator of insulin sensitivity; however, the underlined mechanisms remain unclear. SESN2 is a stress-inducible protein that phosphorylates AMPK in different tissues. In this study, we aimed to validate the amelioration of insulin resistance by globular adiponectin (gAd) and to reveal the role of SESN2 in the improvement of glucose metabolism by gAd. We used a high-fat diet-induced wild-type and SESN2-/- C57BL/6J insulin resistance mice model to study the effects of six-week aerobic exercise or gAd administration on insulin resistance. In vitro study, C2C12 myotubes were used to determine the potential mechanism by overexpressing or inhibiting SESN2. Similar to exercise, six-week gAd administration decreased fasting glucose, triglyceride and insulin levels, reduced lipid deposition in skeletal muscle and reversed whole-body insulin resistance in mice fed on a high-fat diet. Moreover, gAd enhanced skeletal muscle glucose uptake by activating insulin signaling. However, these effects were diminished in SESN2-/- mice. We found that gAd administration increased the expression of SESN2 and Liver kinase B1 (LKB1) and increased AMPK-T172 phosphorylation in skeletal muscle of wild-type mice, while in SESN2-/- mice, LKB1 expression was also increased but the pAMPK-T172 was unchanged. At the cellular level, gAd increased cellular SESN2 and pAMPK-T172 expression. Immunoprecipitation experiment suggested that SESN2 promoted the formation of complexes of AMPK and LKB1 and hence phosphorylated AMPK. In conclusion, our results revealed that SESN2 played a critical role in gAd-induced AMPK phosphorylation, activation of insulin signaling and skeletal muscle insulin sensitization in mice with insulin resistance.

The correlation between the serum LDL-C/Apo B ratio and lumbar bone mineral density in young adults.

BACKGROUND: Numerous studies have confirmed that atherosclerosis is related to osteoporosis (OP), and patients with atherosclerosis are more prone to OP. The ratio of low-density lipoprotein cholesterol (LDL-C) to apolipoprotein B (Apo B) is the valid indicator of atherosclerosis. Nevertheless, conclusions regarding relation between LDL-C/Apo B ratio and bone mineral density (BMD) are still lacking. As a result, this study concentrated on investigating the relationship between LDL-C/Apo B ratio and lumbar BMD in the young adult population according to the National Health and Nutrition Examination Survey (NHANES). METHODS: Information of 2027 young adults (age 20-40 years) from NHANES database was obtained for this cross-sectional study. The correlation between serum LDL-C/Apo B ratio and lumbar BMD was explored through weighted multiple stratified linear regression, while the smooth curve fitting model was utilized for analyzing nonlinear relation. In the nonlinear relation, the inflection point was calculated by saturation threshold analysis. The weighted two-piecewise linear regression model was constructed. RESULTS: After covariates were adjusted, the relation between serum LDL-C/Apo B ratio and lumbar BMD varied by sex (males: ß = -0.0126, 95% CI -0.0892, 0.0640; females: ß = 0.0322, 95% CI -0.0367, 0.1011). By performing age-stratified subgroup analysis, the association also varied by age and sex. Males aged 20-30 years presented a negative trend (ß = -0.0570, 95% CI -0.1656, 0.0517), and males with the age of 31-40 years showed a positive trend (ß = 0.0810, 95% CI -0.0312, 0.1931). Women showed a positive trend by age (females of 20-30 years: ß = 0.0051, 95% CI -0.0935, 0.1036; females of 31-40 years: ß = 0.0265, 95% CI -0.0767, 0.1296). In race-stratified subgroup analysis, the relations varied by sex and race. To be specific, non-Hispanic black males showed a negative trend (ß = -0.0754, 95% CI -0.2695, 0.1188), and males of other races exhibited a positive trend. The trend was positive for women of all races. CONCLUSION: Differences were detected in the association between serum LDL-C/Apo B ratio and lumbar BMD among cases aged 20-40 years across sex, age, and race/ethnicity. In addition, the inflection points in U-shaped relationships were also calculated.

Effects of biodegradation, biotoxicity and microbial community on biostimulation of sulfolane.

To evaluate the effectiveness of biostimulation in remediating soil-free groundwater and groundwater with soil, experiments were conducted using soil and groundwater samples that were contaminated with sulfolane. The main objective was to characterize the differences in sulfolane removal efficiency and biotoxicity between in situ soil-free groundwater and groundwater with soil and different concentrations of dissolved oxygen (1 mg/L and 5 mg/L) and various nutrient salts (in situ and spiked). Optimizing the nutrient salt conditions improved the removal efficiency of sulfolane by 1.8-6.5 that under in situ nutrient salt conditions. Controlling the dissolved oxygen concentration enhanced the efficiency of removal of sulfolane by 1.5-4.5 times over that at the simulated in situ dissolved oxygen concentration, suggesting that the degradation of sulfolane by indigenous microorganisms requires nutrient salts more than it requires dissolved oxygen. Biotoxicity data showed that the luminescence inhibition of Aliivibrio fischeri by sulfolane was lower in the biostimulated samples than in the pre-treated samples. Biostimulation reduced the biotoxicity of the treated samples by 42-51%, revealing that it was effective in removing sulfolane and reducing biotoxicity. Microbial community analysis showed that the biostimulation did not change the dominant species in the original in situ community, and increased the proportion of sulfolane-degraders. The outcome of this study can be used to set parameters for the remediation of groundwater that is contaminated by sulfolane in oil refineries.

Trends of the Global Burden of Disease Attributable to Cannabis Use Disorder in 204 Countries and Territories, 1990-2019: Results from the Disease Burden Study 2019.

Cannabis is the fourth psychoactive substance to be legalized which are of far-reaching significance to the world. We analyzed data from the 2019 Global Burden of Disease Study (GBD) to estimate the incidence and prevalence of cannabis use disorder (CUD) and calculated the disease burden of CUD in 204 countries and territories and 21 regions over the past three decades. We reported disease burden due to CUD in terms of disability-adjusted life years (DALYs), age-standardized rate (ASR), estimated annual percentage change (EAPC), and analyzed associations between the burden of CUD and sociodemographic index (SDI) quintiles. Globally, the number of incidence cases of CUD was estimated to be increasing by 32.3% from 1990 to 2019 and males are nearly double higher than that of female. DALYs increase 38.6% from 1990. Young people aged 20-24 years old with cannabis use disorder have the highest DALYs in 2019, followed by those younger than 20 years old. India, Canada, USA, Qatar, Kenya, and high SDI quintile areas showed a high burden of disease. Nearly 200 million individuals are cannabis users worldwide, and CUD is a notable condition of GBD. The global cultivation of cannabis, rooted in different cultures, diversified access to cannabis, legalization in controversy, the promotion of medical cannabis, and many other factors promote the global cannabis industry is constantly updated and upgraded. It deserves more discussion in the future in terms of pathophysiological mechanisms, socioeconomics, law, and policy improvement. Supplementary Information: The online version contains supplementary material available at 10.1007/s11469-022-00999-4.

Aerobic exercise ameliorates insulin resistance in C57BL/6 J mice via activating Sestrin3.

Skeletal muscle insulin resistance (IR) is closely linked to hyperglycemia and metabolic disorders. Regular exercise enhances insulin sensitivity in skeletal muscle, but its underlying mechanisms remain unknown. Sestrin3 (SESN3) is a stress-inducible protein that protects against obesity-induced hepatic steatosis and insulin resistance. Regular exercise training is known to increase SESN3 expression in skeletal muscle. The purpose of this study was to explore whether SESN3 mediates the metabolic effects of exercise in the mouse model of high-fat diet (HFD)-induced IR. SESN3-/- mice exhibited severer body weight gain, ectopic lipid accumulation, and dysregulation of glucose metabolism after long-term HFD feeding compared with the wild-type (WT) mice. Moreover, we found that SESN3 deficiency weakened the effects of exercise on reducing serum insulin levels and improving glucose tolerance in mice. Exercise training increased pAKT-S473 and GLUT4 expression, accompanied by enhanced pmTOR-S2481 (an indicator of mTORC2 activity) in WT quadriceps that were less pronounced in SESN3-/- mice. SESN3 overexpression in C2C12 myotubes further confirmed that SESN3 played an important role in skeletal muscle glucose metabolism. SESN3 overexpression increased the binding of Rictor to mTOR and pmTOR-S2481 in C2C12 myotubes. Moreover, SESN3 overexpression resulted in an elevation of glucose uptake and a concomitant increase of pAKT-S473 in C2C12 myotubes, whereas these effects were diminished by downregulation of mTORC2 activity. Taken together, SESN3 is a crucial protein in amplifying the beneficial effects of exercise on insulin sensitivity in skeletal muscle and systemic glucose levels. SESN3/mTORC2/AKT pathway mediated the effects of exercise on skeletal muscle insulin sensitivity.

Inflammation and Late-Life Depression: Unraveling the Complex Relationship and Potential Therapeutic Strategies.

The origins of late-life depression are multifaceted and remain challenging to fully understand. While the traditional monoamine neurotransmitter hypothesis provides some insights, it falls short in explaining the disease's onset and progression, leaving treatments often less than optimal. There is an emergent need to uncover new underlying mechanisms. Among these, the "inflammation hypothesis" has been gaining traction in scientific discussions regarding late-life depression. There is compelling evidence linking inflammation processes to the emergence of this form of depression. This review delves into the nuanced relationship between inflammation and late-life depression, emphasizing the pivotal role and implications of inflammation in its pathogenesis. Changes in Ca2+ homeostasis, cytokine levels, brain-derived neurotrophic factor (BDNF), white cell ratios, and the involvement of the NOD-, LRR-, and Pyrin domain-containing protein 3 (NLRP3) inflammasome have all been suggested as potential biomarkers that tie inflammation to late-life depression. Furthermore, factors such as aging-induced DNA damage, oxidative stress, mitochondrial impairments, disruptions in the hypothalamic-pituitary-adrenal axis, activated microglia and associated neuroinflammation, as well as the gut-brain axis dynamics, could serve as bridges between inflammation and depression. Deepening our understanding of these connections could usher in innovative anti-inflammatory treatments and strategies for late- life depression.

Topological mechanism in the nonlinear power-law relaxation of cell cortex.

Different types of cells exhibit a universal power-law rheology, but the mechanism underneath is still unclear. Based on the exponential distribution of actin filament length, we treat the cell cortex as a collection of chains of crosslinkers with exponentially distributed binding energy, and show that the power-law exponent of its stress relaxation should scale with the chain length. Through this model, we are able to explain how the exponent can be regulated by the crosslinker number and imposed strain during cortex relaxation. Network statistics show that the average length of filament-crosslinker chains decreases with the crosslinker number, which endows a denser network with lower exponent. Due to gradual molecular alignment with the stretch direction, the number of effectively stretched crosslinkers in the network is found to increase with the imposed strain. This effective growth in network density diminishes the exponent under large strain. By incorporating the inclined angle of crosslinkers into the model without in-series structure, we show that the exponent cannot be altered by crosslinker rotation directly, refining our previous conjectures. This work may help to understand cellular mechanics from the molecular perspective.

Chang impact analysis of level 3 COVID-19 alert on air pollution indicators using artificial neural network.

In this study, mean monthly and diurnal variations in fine particulate matters (PM2.5), nitrate, sulfate, and gaseous precursors were investigated during the Level 3 COVID-19 alert from May 19 to July 27 in 2021. For comparison, the historical data during the identical period in 2019 and 2020 were also provided to determine the effect of the Level 3 COVID-19 alert on aerosols and gaseous pollutants concentrations in Taichung City. A machine learning model using the artificial neural network technique coupled with a kinetic model was applied to predict NOx, O3, nitrate (NO3 -), and sulfate (SO4 2-) to investigate potential emission sources and chemical reaction mechanism. D during the Level 3 COVID-19 alert, a decrease in NOx concentration due to a decrease in traffic flow under the NOx-saturated regime was observed to enhance the secondary NO3 - and O3 formation. The present models were shown to predict 80.1, 77.0, 72.6, and 67.2% concentrations of NOx, O3, NO3 -, and SO4 2-, respectively, which could help decision-makers for pollutant emissions reduction policies development and air pollution control strategies. It is recommended that more long-term datasets, including water soluble inorganic salts (WIS), precursors including OH radicals, NH3, HNO3, and H2SO4, be provided by regulatory air quality monitoring stations to further improve the prediction model accuracy.

Depression and anxiety of medical students at Kunming Medical University during COVID-19: A cross-sectional survey.

An isolation strategy was used to control the transmission and rapid spread of COVID-19 in Yunnan. As a result, students were supposed to stay at home and disrupted their outside activities. It led to a detrimental influence on students' mental health. The purpose of this study was to investigate the prevalence and risk factors of depression and anxiety among medical students and to provide ideas for the prevention of depression and anxiety in medical students. A cross-sectional survey was conducted among 2,116 medical students at Kunming Medical University from July 8 to July 16, 2020. Participants' demographic and living conditions were collected. Depression and anxiety were measured using the Patient Health Questionnaire 9 and General Anxiety Disorder-7, respectively. Uni- and multivariate logistic regression analyses were performed to detect risk factors associated with depression and anxiety. The prevalence rates of depression and anxiety among medical students were 52.5 and 29.6%, respectively. Depression was more likely to be caused by low grades, lack of physical exercise, drug use, irregular diet, extensive screen time on mobile phones, being greatly affected by the COVID-19 pandemic, and inadaptability to offline courses. Anxiety was more likely to be caused by lack of physical exercise, drug use, irregular diet, and inadaptability to offline courses. Depression and anxiety are highly comorbid. Our study showed predictive factors for depression and anxiety and identified a major mental health burden on medical students during the COVID-19 outbreak. More targeted measures should be taken to improve the mental state of students to reduce the incidence of depression and anxiety.