Effective treatment of corticosteroid-induced facial erythema using fractional radiofrequency microneedling.

OBJECTIVES: To investigate the efficacy of Fractional Radiofrequency Microneedling (FRM) in treating corticosteroid-induced facial erythema. METHODS: A retrospective study was conducted involving eight patients diagnosed as corticosteroid-induced facial erythema. Each patient underwent a single session of FRM. Evaluative measures included Clinician's Erythema Assessment (CEA), Patient's Self-Assessment (PSA), assessment of telangiectasia severity, procedure-associated pain (10-point scale), patient satisfaction (3-point scale) and secondary outcomes. RESULTS: The study found a 75% success rate and 100% effectiveness rate in alleviating erythema symptoms. CEA and PSA scores decreased by 67.7% and 78.1%, respectively. No cases of erythema rebound were recorded during the 3-month follow-up period. CONCLUSIONS: FRM demonstrated effectiveness and safety in treating facial erythema, offering promising advancement in dermatologic therapeutics.

The transcription factor PbbHLH164 is destabilized by PbRAD23C/D.1 and mediates ethylene biosynthesis during pear fruit ripening.

The phytohormone ethylene plays an important role in climacteric fruit ripening. However, the knowledge on molecular regulation of ethylene biosynthesis remains limited in pear fruit. Herein, a new basic helix-loop-helix transcription factor, PbbHLH164, was identified based on the transcriptome analysis of different developing and ripening fruits of two pear cultivars 'Sucui No. 1' and 'Cuiguan'. PbbHLH164 was more highly expressed in ripening fruit than in developing fruit and positively correlated with ethylene production in both cultivars. PbbHLH164 could directly bind to the promoter of 1-aminocyclopropane-1-carboxylate synthase, PbACS1b, to enhance the expression, leading to the increase of ethylene production and the acceleration of fruit ripening. Interestingly, PbbHLH164 physically interacted with an ubiquitin-like/ubiquitin-associated protein PbRAD23C/D.1, and the interaction of PbbHLH164 with PbRAD23C/D.1 attenuated the function of PbbHLH164 in enhancing the activity of the PbACS1b promoter. Notably, PbRAD23C/D.1 was involved in the degradation of PbbHLH164, and this degradation was inhibited by an ubiquitin proteasome inhibitor MG132. Different from PbbHLH164, PbRAD23C/D.1 was more highly expressed in developing fruit than in ripening fruit of both cultivars. These results suggest that the increase of ethylene production during pear fruit ripening results from the up-regulated expression of PbbHLH164 and the down-regulated expression of PbRAD23C/D.1. This information provided new insights into the molecular regulation of ethylene biosynthesis during fruit ripening.

Human umbilical cord derived mesenchymal stem cells overexpressing HO-1 attenuate neural injury and enhance functional recovery by inhibiting inflammation in stroke mice.

AIMS: The current evidence demonstrates that mesenchymal stem cells (MSCs) hold therapeutic potential for ischemic stroke. However, it remains unclear how changes in the secretion of MSC cytokines following the overexpression of heme oxygenase-1 (HO-1) impact excessive inflammatory activation in a mouse ischemic stroke model. This study investigated this aspect and provided further insights. METHODS: The middle cerebral artery occlusion (MCAO) mouse model was established, and subsequent injections of MSC, MSCHO-1 , or PBS solutions of equal volume were administered via the mice's tail vein. Histopathological analysis was conducted on Days 3 and 28 post-MCAO to observe morphological changes in brain slices. mRNA expression levels of various factors, including IL-1ß, IL-6, IL-17, TNF-α, IL-1Ra, IL-4, IL-10, TGF-ß, were quantified. The effects of MSCHO-1 treatment on neurons, microglia, and astrocytes were observed using immunofluorescence after transplantation. The polarization direction of macrophages/microglia was also detected using flow cytometry. RESULTS: The results showed that the expression of anti-inflammatory factors in the MSCHO-1 group increased while that of pro-inflammatory factors decreased. Small animal fluorescence studies and immunofluorescence assays showed that the homing function of MSCsHO-1 was unaffected, leading to a substantial accumulation of MSCsHO-1 in the cerebral ischemic region within 24 h. Neurons were less damaged, activation and proliferation of microglia were reduced, and polarization of microglia to the M2 type increased after MSCHO-1 transplantation. Furthermore, after transplantation of MSCsHO-1 , the mortality of mice decreased, and motor function improved significantly. CONCLUSION: The findings indicate that MSCs overexpressing HO-1 exhibited significant therapeutic effects against hyper-inflammatory injury after stroke in mice, ultimately promoting recovery after ischemic stroke.

Chromosomal Instability-Driven Cancer Progression: Interplay with the Tumour Microenvironment and Therapeutic Strategies.

Chromosomal instability (CIN) is a prevalent characteristic of solid tumours and haematological malignancies. CIN results in an increased frequency of chromosome mis-segregation events, thus yielding numerical and structural copy number alterations, a state also known as aneuploidy. CIN is associated with increased chances of tumour recurrence, metastasis, and acquisition of resistance to therapeutic interventions, and this is a dismal prognosis. In this review, we delve into the interplay between CIN and cancer, with a focus on its impact on the tumour microenvironment-a driving force behind metastasis. We discuss the potential therapeutic avenues that have resulted from these insights and underscore their crucial role in shaping innovative strategies for cancer treatment.

Correlation between conventional ultrasound features combined with contrast-enhanced ultrasound patterns and pathological prognostic factors in malignant non-mass breast lesions.

OBJECTIVE: To investigate the correlation between ultrasound performance and prognostic factors in malignant non-mass breast lesions (NMLs). MATERIALS AND METHODS: This study included 106 malignant NMLs in 104 patients. Different US features and contrast enhancement patterns were evaluated. Prognostic factors, including histological types and grades, axillary lymph node and peritumoral lymphovascular status, estrogen and progesterone receptor status and the expression of HER-2 and Ki-67 were determined. A chi-square test and logistic regression analysis were used to analyse possible associations. RESULTS: Lesion size (OR: 3.08, p = 0.033) and posterior echo attenuation (OR: 8.38, p < 0.001) were useful in reflecting malignant NMLs containing an invasive carcinoma component. Posterior echo attenuation (OR: 7.51, p = 0.003) and unclear enhancement margin (OR: 6.50, p = 0.018) were often found in tumors with axillary lymph node metastases. Peritumoural lymphovascular invasion mostly exhibited posterior echo attenuation (OR: 3.84, p = 0.049) and unclear enhancement margin (OR: 8.68, p = 0.042) on ultrasound images. Perfusion defect was a comparatively accurate enhancement indicator for negative ER (OR: 2.57, p = 0.041) and PR (OR: 3.04, p = 0.008) expression. Calcifications (OR: 3.03, p = 0.025) and enlarged enhancement area (OR: 5.36, p = 0.033) imply an increased risk of positive HER-2 expression. Similarly, Calcifications (OR: 4.13, p = 0.003) and enlarged enhancement area (OR: 11.05, p < 0.001) were valid predictors of high Ki-67 proliferation index. CONCLUSION: Ultrasound performance is valuable for non-invasive prediction of prognostic factors in malignant NMLs.

Twitter Sentiment Geographical Index Dataset.

Promoting well-being is one of the key targets of the Sustainable Development Goals at the United Nations. Many national and city governments worldwide are incorporating Subjective Well-Being (SWB) indicators into their agenda, to complement traditional objective development and economic metrics. In this study, we introduce the Twitter Sentiment Geographical Index (TSGI), a location-specific expressed sentiment database with SWB implications, derived through deep-learning-based natural language processing techniques applied to 4.3 billion geotagged tweets worldwide since 2019. Our open-source TSGI database represents the most extensive Twitter sentiment resource to date, encompassing multilingual sentiment measurements across 164 countries at the admin-2 (county/city) level and daily frequency. Based on the TSGI database, we have created a web platform allowing researchers to access the sentiment indices of selected regions in the given time period.

Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition.

Estimating health benefits of reducing fossil fuel use from improved air quality provides important rationales for carbon emissions abatement. Simulating pollution concentration is a crucial step of the estimation, but traditional approaches often rely on complicated chemical transport models that require extensive expertise and computational resources. In this study, we develop a machine learning framework that is able to provide precise and robust annual average fine particle (PM2.5) concentration estimations directly from a high-resolution fossil energy use dataset. Applications of the framework with Chinese data reveal highly heterogeneous health benefits of avoiding premature mortality by reducing fossil fuel use in different sectors and regions in China with a mean of $19/tCO2 and a standard deviation of $38/tCO2. Reducing rural and residential coal use offers the highest co-benefits with a mean of $151/tCO2. Our findings prompt careful policy designs to maximize cost-effectiveness in the transition toward a carbon-neutral energy system.

Distinct Classes of Statistical Anxiety: Latent Profile and Network Psychometrics Analysis of University Students.

Purpose: Many university students will experience statistical anxiety. Consequentially, the relationship between such anxiety and learning performance has been of concern to various educational researchers. To date, however, there has been no consistent resolution to this problem. Because previous studies have mainly used the perspective of variant-centered analysis rather than taking into account individual differences, this study argues that the different classes of statistical anxiety among university students may be an important influencing factor. Participants and Methods: In this study, 1607 Chinese university students who had just completed a statistics course were assessed using the Statistical Anxiety Scale, Statistics Learning Self-Efficacy Scale, and Learning Engagement Scale, and an exploratory study was conducted to determine whether university students' statistical anxiety could be divided into different classes. Latent profile and network psychometrics analyses were then used to analyze the data. Results: (1) The latent profile analysis found that university students' statistical anxiety could be divided into three different latent classes: mild test anxiety, moderate text anxiety, and severe statistical anxiety. (2) The correlation analysis showed that the relationships among the three latent classes of statistical anxiety and learning performance were not entirely consistent, indicating that there was heterogeneity in the statistical anxiety of these university students. (3) Further network psychometrics analysis showed that the statistical anxiety network structure of the three latent classes has different core nodes that reflected the most important symptoms of statistical anxiety. Conclusion: There is heterogeneity in university students' statistical anxiety that can be divided into three latent classes. These core nodes in the statistical anxiety networks of the three latent classes were different, helping statistics instructors to better understand the nature of these latent classes, take different intervention measures for different latent classes of university students.

The role of alerting in the attentional boost effect.

Stimuli presented simultaneously with behaviorally relevant events (e.g., targets) are better memorized, an unusual effect defined as the attentional boost effect (ABE). We hypothesized that all types of behaviorally relevant events, including attentional cues, can promote the encoding process for the stimuli paired with them, and the attentional alerting network can amplify the ABE. The two experiments we conducted demonstrated that not all behaviorally relevant events, including alerting cues, benefit the processing of concurrently paired stimuli. We also found that the presence of a cue prior to a target can extend the memory advantage produced by target detection, but this advantage can only be observed within a limited range of time. Overall, our study provides the first evidence that the alerting network plays an important role in the ABE.

Positive effects of napping on memory consolidation and resistance against interference.

PURPOSE: This study explored the relationship between naps and memory among habitual nappers in China. METHODS: Medical college students participated and were divided into 30-min, 60-min, and 90-min time-in-bed groups. To evaluate declarative and procedural memory performance, A-B and A-C interfering word pair and interfering finger tapping tasks were employed. RESULTS: Among 60 students, a significant decrease in the correct recall rate in the declarative task after having a nap was found only in the 30-min group (p = 0.005). After learning interference (A-C word pairs), the correct recall rate for the declarative task decreased significantly in all interference tests (ps < 0.001). In the procedural task, the speed of sequence A in the retests increased after having a nap in all three groups (ps < 0.048), with a significant decrease in accuracy only in the 30-min group (p = 0.042). After learning interference (sequence B) in the procedural task, the speed of sequence A increased in the 60-min group after 1 h (p = 0.049), and both the 60-min and 90-min groups showed increased speed after one night (ps < 0.022). No significant improvement in speed was found in the 30-min group (ps > 0.05), and this group showed the lowest accuracy for sequence A (ps < 0.16). CONCLUSION: A habitual nap time-in-bed of 60 or 90 min had better effects on declarative and procedural memory consolidation and better memory resistance against interference in procedural memory.

Intraday adaptation to extreme temperatures in outdoor activity.

Linkages between climate and human activity are often calibrated at daily or monthly resolutions, which lacks the granularity to observe intraday adaptation behaviors. Ignoring this adaptation margin could mischaracterize the health consequences of future climate change. Here, we construct an hourly outdoor leisure activity database using billions of cell phone location requests in 10,499 parks in 2017 all over China to investigate the within-day outdoor activity rhythm. We find that hourly temperatures above 30 °C and 35 °C depress outdoor leisure activities by 5% (95% confidence interval, CI 3-7%) and by 13% (95% CI 10-16%) respectively. This activity-depressing effect is larger than previous daily or monthly studies due to intraday activity substitution from noon and afternoon to morning and evening. Intraday adaptation is larger for locations and dates with time flexibility, for individuals more frequently exposed to heat, and for parks situated in urban areas. Such within-day adaptation substantially reduces heat exposure, yet it also delays the active time at night by about half an hour, with potential side effect on sleep quality. Combining empirical estimates with outputs from downscaled climate models, we show that unmitigated climate change will generate sizable activity-depressing and activity-delaying effects in summer when projected on an hourly resolution. Our findings call for more attention in leveraging real-time activity data to understand intraday adaptation behaviors and their associated health consequences in climate change research.

Transcriptome Analysis Revealed Potential Neuro-Immune Interaction in Papillary Thyroid Carcinoma Tissues.

BACKGROUND: A recent study reported that papillary thyroid carcinoma (PTC) was associated with increased adrenergic nerve density. Meanwhile, emerging evidence suggested that tumor-innervating nerves might play a role in shaping the tumor microenvironment. We aimed to explore the potential interaction between neuronal markers and tumor microenvironmental signatures through a transcriptomic approach. METHODS: mRNA sequencing was conducted using five pairs of PTC and adjacent normal tissues. The Gene Set Variation Analysis (GSVA) was performed to calculate enrichment scores of gene sets related to tumor-infiltrating immune cells and the tumor microenvironment. The potential interaction was tested using the expression levels of a series of neuronal markers and gene set enrichment scores. RESULTS: PTC tissues were associated with increased enrichment scores of CD8 T cells, cancer-associated fibroblasts, mast cells, and checkpoint molecules. The neuronal marker for cholinergic neurons was positively correlated with CD8 T cell activation, while markers for serotonergic and dopaminergic neurons showed an inverse correlation. CONCLUSION: Distinct neuronal markers exerted different correlations with tumor microenvironmental signatures. Tumor-innervating nerves might play a role in the formation of the PTC microenvironment.

Non-mass Breast Lesions: Could Multimodal Ultrasound Imaging Be Helpful for Their Diagnosis?

Objective: To develop a prediction model for discriminating malignant from benign breast non-mass-like lesions (NMLs) using conventional ultrasound (US), strain elastography (SE) of US elastography and contrast-enhanced ultrasound (CEUS). Methods: A total of 101 NMLs from 100 patients detected by conventional US were enrolled in this retrospective study. The characteristics of NMLs in conventional US, SE and CEUS were compared between malignant and benign NMLs. Histopathological results were used as the reference standard. Binary logistic regression analysis was performed to identify the independent risk factors. A multimodal method to evaluate NMLs based on logistic regression was developed. The diagnostic performance of conventional US, US + SE, US + CEUS and the combination of these modalities was evaluated and compared. Results: Among the 101 lesions, 50 (49.5%) were benign and 51 (50.5%) were malignant. Age ≥45 y, microcalcifications in the lesion, elasticity score >3, earlier enhancement time and hyper-enhancement were independent diagnostic indicators included to establish the multimodal prediction method. The area under the receiver operating characteristic curve (AUC) of US + SE + CEUS was significantly higher than that of US (p < 0.0001) and US + SE (p < 0.0001), but there was no significant difference between the AUC of US + SE + CEUS and the AUC of US + CEUS (p = 0.216). Conclusion: US + SE + CEUS and US + CEUS could significantly improve the diagnostic efficiency and accuracy of conventional US in the diagnosis of NMLs.

Predictive Values of sTREM-1, PCT and CRP for Multiple Trauma-Induced Acute Respiratory Distress Syndrome Complicated with Pulmonary Infection.

BACKGROUND: The aim is to investigate the predictive values of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), procalcitonin (PCT) and C-reactive protein (CRP) for multiple trauma-induced acute respiratory distress syndrome (ARDS) complicated with pulmonary infection. METHODS: One hundred and twelve patients with multiple trauma-induced ARDS admitted from April 2019 to April 2021 were selected and divided into infection group (n = 49) and non-infection group (n = 63). Their general data and laboratory test indicators were compared. Multivariate logistic regression analysis was utilized to identify the influencing factors for concurrent pulmonary infection. Pearson's analysis was employed to analyze the correlations of sTREM-1, PCT, and CRP with other influencing factors. The predictive values of sTREM-1, PCT, and CRP for pulmonary infection in ARDS patients were evaluated using receiver operating characteristic (ROC) curves. Based on the cutoff values, the patients were assigned to low-, medium-, and high-risk groups. Survival curves were plotted by Kaplan-Meier method to compare the 28-day survival. RESULTS: The infection group had significantly higher injury severity score (ISS), Acute Physiology and Chronic Health Evaluation II (APACHE II) score, sTREM-1, PCT, CRP, and macrophage inflammatory protein (MIP)-1α, longer tracheal intubation time and intensive care unit (ICU) stay time, and lower oxygenation index than those of the non-infection group (p < 0.05). Multivariate logistic regression analysis revealed that increased ISS, APACHE II score, as well as elevated blood concentrations of sTREM-1, PCT, CRP and MIP-1α were risk factors for concurrent pulmonary infection (p < 0.05). sTREM-1, PCT, and CRP were positively correlated with ISS, APACHE II score, and MIP-1α (p < 0.05). The areas under ROC curves of sTREM-1, PCT, CRP, and their combination were 0.795, 0.784, 0.756, and 0.860, respectively (p < 0.001), indicating high predictive values. The survival rate of the high-risk group (46.43%) was significantly lower than those of the low-risk group (89.58%) and medium-risk group (75.00%) (p < 0.05). CONCLUSIONS: sTREM-1, PCT, and CRP are highly expressed in serum of patients with multiple trauma-induced ARDS complicated with pulmonary infection. The combined detection of three markers is of high predictive value.

Measuring daily-life fear perception change: A computational study in the context of COVID-19.

COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people's daily life and thus evoked intensive psychological responses indirect to COVID-19 infections. In this study, we construct a panel expressed fear database tracking the universe of social media posts (16 million) generated by 536 thousand individuals between January 1st, 2019 and August 31st, 2020 in China. We employ deep learning techniques to detect expressions of fear emotion within each post, and then apply topic model to extract the major topics of fear expressions in our sample during the COVID-19 pandemic. Our unique database includes a comprehensive list of topics, not being limited to post centering around COVID-19. Based on this database, we find that sleep disorders ("nightmare" and "insomnia") take up the largest share of fear-labeled posts in the pre-pandemic period (January 2019-December 2019), and significantly increase during the COVID-19. We identify health and work-related concerns are the two major sources of non-COVID fear during the pandemic period. We also detect gender differences, with females having higher fear towards health topics and males towards monetary concerns. Our research shows how applying fear detection and topic modeling techniques on posts unrelated to COVID-19 can provide additional policy value in discerning broader societal concerns during this COVID-19 crisis.

Beam manipulation for quantum dot light-emitting diode with an Ag grating and a phase-gradient metasurface.

The quantum dot (QD) light-emitting diode (LED) is a robust scheme for single photon source. However, the spontaneous emission of a QD LED has arbitrary directions and polarizations, which is disadvantage for photon collection and manipulation. We propose a QD LED integrated with an Ag grating and a phase-gradient metasurface. The circular patterned Ag grating is adopted to collimate the emission beam with right phase and improve its spatial coherence, therefore a phase-gradient metasurface can work for beam manipulation. The 10°, 20°, and 30° angle deflection as well as doughnut-pattern generation are demonstrated by numerical simulation. A small metasurface with the width of 6 µm can provide a collection efficiency of 25.9% at the deflection angle of 10°. Furthermore, only one single QD can be selected from a QD assembly with a low-density.

Metabolic Engineering of the Isopentenol Utilization Pathway Enhanced the Production of Terpenoids in Chlamydomonas reinhardtii.

Eukaryotic green microalgae show considerable promise for the sustainable light-driven biosynthesis of high-value fine chemicals, especially terpenoids because of their fast and inexpensive phototrophic growth. Here, the novel isopentenol utilization pathway (IUP) was introduced into Chlamydomonas reinhardtii to enhance the hemiterpene (isopentenyl pyrophosphate, IPP) titers. Then, diphosphate isomerase (IDI) and limonene synthase (MsLS) were further inserted for limonene production. Transgenic algae showed 8.6-fold increase in IPP compared with the wild type, and 23-fold increase in limonene production compared with a single MsLS expressing strain. Following the culture optimization, the highest limonene production reached 117 µg/L, when the strain was cultured in a opt2 medium supplemented with 10 mM isoprenol under a light: dark regimen. This demonstrates that transgenic algae expressing the IUP represent an ideal chassis for the high-value terpenoid production. The IUP will facilitate further the metabolic and enzyme engineering to enhance the terpenoid titers by significantly reducing the number of enzyme steps required for an optimal biosynthesis.

Integration of protein context improves protein-based COVID-19 patient stratification.

BACKGROUND: Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. METHODS: We performed machine learning based on three previously published datasets. The first was a SWATH (sequential window acquisition of all theoretical fragment ion spectra) MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort. RESULTS: Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M (alpha-2-macroglobulin) were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type. CONCLUSION: Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

Global evidence of expressed sentiment alterations during the COVID-19 pandemic.

The COVID-19 pandemic has created unprecedented burdens on people's physical health and subjective well-being. While countries worldwide have developed platforms to track the evolution of COVID-19 infections and deaths, frequent global measurements of affective states to gauge the emotional impacts of pandemic and related policy interventions remain scarce. Using 654 million geotagged social media posts in over 100 countries, covering 74% of world population, coupled with state-of-the-art natural language processing techniques, we develop a global dataset of expressed sentiment indices to track national- and subnational-level affective states on a daily basis. We present two motivating applications using data from the first wave of COVID-19 (from 1 January to 31 May 2020). First, using regression discontinuity design, we provide consistent evidence that COVID-19 outbreaks caused steep declines in expressed sentiment globally, followed by asymmetric, slower recoveries. Second, applying synthetic control methods, we find moderate to no effects of lockdown policies on expressed sentiment, with large heterogeneity across countries. This study shows how social media data, when coupled with machine learning techniques, can provide real-time measurements of affective states.