Factors Associated with Alternate Level of Care Status Designation: a Case-Control Study and Model to Optimize Care Trajectories.

Background: As health-care demand is growing, our health-care system will require the optimization of the care trajectories. Patients with an alternate level of care (ALC) status could be a target for flow optimization. We aimed to characterize ALC patients and risk factors for ALC status, and to propose an integrated model to analyze the trajectory of ALC patients and discuss solutions to reduce their burden. Methods: A case-control design was used to compare 60 ALC and 60 non-ALC patients admitted to the geriatric unit of the Centre hospitalier de l'Université de Montréal in 2021, collecting medical and sociodemographic data. Based on our model, univariate statistical analyses were computed to compare groups and identify risk factors for ALC status. Results: ALC patients were less independent (22% performed five to six activities of daily living vs. 43%, p = .03). Both groups were comparable in terms of mobility and neurocognitive disorders. ALC patients were more likely to receive a new diagnosis of a neurocognitive disorder or new behavioural or psychological symptoms (37% vs. 15%, p = .008). Up to 25% of ALC patients were admitted despite presenting no active medical condition (vs. 3% of non-ALC patients, p = .002). Conclusions: The optimization of the care trajectory of ALC patients is mainly based on pre-hospital and post-hospital factors. A proportion of ALC admissions might be avoidable with additional investment in home care resources and relocation procedures. Fluidity of ALC trajectory may benefit from improved orientation at discharge procedures. Full optimization of ALC trajectories requires a systemic understanding of the health-care system.

Synthesis of Nano Sulfur/Chitosan-Copper Complex and Its Nematicidal Effect against Meloidogyne incognita In Vitro and on Coffee Pots.

Sulfur is one of the inorganic elements used by plants to develop and produce phytoalexin to resist certain diseases. This study reported a method for preparing a material for plant disease resistance. Sulfur nanoparticles (SNPs) stabilized in the chitosan-Cu2+ (CS-Cu2+) complex were synthesized by hydrolysis of Na2S2O3 in an acidic medium. The obtained SNPs/CS-Cu2+ complex consisting of 0.32% S, 4% CS, and 0.7% Cu (w/v), contained SNPs with an average size of ~28 nm as measured by transmission electron microscopy images. The X-ray diffraction pattern of the SNPs/CSCu2+ complex showed that SNPs had orthorhombic crystal structures. Interaction between SNPs and the CS-Cu2+ complex was also investigated by ultraviolet-visible. Results in vitro nematicidal effect of materials against Meloidogyne incognita showed that SNPs/CS-Cu2+ complex was more effective in killing second-stage juveniles (J2) nematodes and inhibiting egg hatching than that of CS and CS-Cu2+ complex. The values of LC50 in killing J2 nematodes and EC50 in inhibiting egg hatching of SNPs/CS-Cu2+ complex were 75 and 51 mg/l, respectively. These values were lower than those of CS and the CS-Cu2+ complex. The test results on the nematicidal effect against M. incognita on coffee pots showed that the SNPs/CS-Cu2+ complex was 100% effective at a concentration of 150 mg/l. Therefore, the SNPs/CS-Cu2+ complex could be considered as a biochemical material with potential for agricultural applications to control root-knot nematodes.

Metasurface-based dual-sense circularly polarized antenna for MIMO/full-duplex applications.

This paper introduces a two-element antenna array with dual-sense circular polarization, wideband operation, and high isolation characteristics. The antenna consists of two conventional truncated corner patches and an extra layer of metasurface (MS) located above the radiating patches. The overall dimensions of the proposed antenna are 0.92 λ0 × 0.73 λ0 × 0.05 λ0 and the element spacings are 0.02 λ0 and 0.39 λ0 with respect to edge-to-edge and center-to-center spacings. For validation, measurements on a fabricated antenna prototype are carried out. The measured data demonstrate that the presented MS-based antenna has a wide operating bandwidth of 14.5% with high isolation of better than 26 dB. The excellent performance could be concluded from the results of the investigation, which indicates that the proposed MS-based antenna could be a good candidate for multiple-input multiple-output (MIMO) and full-duplex applications.

Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains.

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR.

A highly selective KIT inhibitor MOD000001 suppresses IgE-mediated mast cell activation.

Background: The KIT receptor tyrosine kinase and its ligand, stem cell factor (SCF), control proliferation and survival of mast cells. Thus, targeting KIT signaling may show promise for the treatment of allergic diseases involving mast cells. Recently, we discovered a new compound, MOD000001, as a potential small-molecule KIT kinase inhibitor by using an in silico approach. Objective: We sought to determine whether MOD000001 is highly selective to KIT, inhibits KIT signaling in mast cells, and affects IgE-mediated mast cell activation. Methods: The interaction of MOD000001 with 468 human kinases and its inhibitory activity against KIT were profiled and evaluated by using KINOMEscan (Discover X/Eurofins Corporation, Fremont, Calif) and cell-free kinase assays, respectively. The effects of MOD000001 on SCF-dependent signaling were examined by using primary mouse and human mast cells. The effects of MOD000001 on SCF-induced degranulation and passive cutaneous anaphylaxis reaction were examined in mice. Results: MOD000001 interacted with KIT and inhibited KIT kinase activity with high selectivity. MOD000001 suppressed SCF-induced KIT signaling in mouse and human mast cells and in mice. Passive cutaneous anaphylaxis reaction was suppressed in mice treated with MOD000001 both for a short-term (1 week) and for a long-term (7 weeks). Mice treated with MOD000001 for a long-term, but not for a short-term, showed skin mast cell reduction. Conclusions: MOD000001 is a highly selective KIT inhibitor that can suppress IgE-mediated mast cell activation in vivo. MOD000001 may do so by reducing tissue mast cell numbers or by other unknown mechanisms. The findings suggest potential benefits of MOD000001 for allergic diseases involving IgE-mediated mast cell activation.

Influence of purple non-sulfur bacterial augmentation on soil nutrient dynamics and rice (Oryza sativa) growth in acidic saline-stressed environments.

The aim of the current study was to assess the potency of the exopolymeric substances (EPS)-secreting purple non-sulfur bacteria (PNSB) on rice plants on acidic salt-affected soil under greenhouse conditions. A two-factor experiment was conducted following a completely randomized block design. The first factor was the salinity of the irrigation, and the other factor was the application of the EPS producing PNSB (Luteovulum sphaeroides EPS18, EPS37, and EPS54), with four replicates. The result illustrated that irrigation of salt water at 3-4‰ resulted in an increase in the Na+ accumulation in soil, resulting in a lower rice grain yield by 12.9-22.2% in comparison with the 0‰ salinity case. Supplying the mixture of L. sphaeroides EPS18, EPS37, and EPS54 increased pH by 0.13, NH4+ by 2.30 mg NH4+ kg-1, and available P by 8.80 mg P kg-1, and decreased Na+ by 0.348 meq Na+ 100 g-1, resulting in improvements in N, P, and K uptake and reductions in Na uptake, in comparison with the treatment without bacteria. Thus, the treatments supplied with the mixture of L. sphaeroides EPS18, EPS37, and EPS54 resulted in greater yield by 27.7% than the control treatment.

Recent progress in machine learning approaches for predicting carcinogenicity in drug development.

INTRODUCTION: This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance. AREAS COVERED: The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments. It explores applications ranging from early-phase compound screening to clinical trial optimization, highlighting the versatility of ML in enhancing predictive accuracy and efficiency. EXPERT OPINION: Through the analysis of traditional approaches such as in vivo rodent bioassays and in vitro assays, the review underscores the limitations and resource intensity associated with these methods. It provides expert insights into how ML offers innovative solutions to address these challenges, revolutionizing safety assessments in drug development.

Oral hygiene status and oral care motivation in children aged 7-9 years in a Vietnam primary school: A cross-sectional study.

INTRODUCTION: Oral hygiene is a crucial factor in oral health, especially in children. To increase the awareness of oral care behaviour among children, oral care motivation plays a critical role in daily dental practices. Therefore, this cross-sectional study was conducted to investigate the current oral hygiene status and evaluate the association between oral care motivation and oral hygiene index in 7-9-year-old children at Primary School in Hanoi, Vietnam. METHODS: Clinical examinations were performed on 200 randomly selected children to assess the Simplified Oral Hygiene Index (OHI-S). Face-to-face interviews were applied to record students' intrinsic and extrinsic motivation for oral care through a questionnaire, which consisted of questions regarding demographic characteristics and oral care motivation. Data were analyzed using STATA 15.0 software and a p-value < 0.05 was statistically significant. RESULTS: The mean OHI-S score was 2.48 ± 0.72. Good and fair oral hygiene were observed in 7.5 % and 66 % of participants, respectively. Students' motivation for dental care was predominantly extrinsic, with a mean score of 15.87 ± 1.322. Higher motivation in dental care is statistically significantly associated with oral hygiene index score (Coef=-0.27). CONCLUSION: These results indicate that students with intrinsic motivation exhibit better oral hygiene practices. Consequently, strengthening oral health educational programs in primary schools based on intrinsic motivation could be helped improve the oral hygiene status and oral care behaviours of children.

Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study.

BACKGROUND: Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commercially available wearable (SmartCare) utilising photoplethysmography (PPG) to stratify clinical risk for a cohort of hospitalised patients with dengue in Vietnam. METHODS: We performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified between 3 severity classes: i) uncomplicated (ward-based), ii) moderate-severe (emergency department-based), and iii) severe (ICU-based). Data from paediatric patients were split into 2 classes: i) severe (during ICU stay) and ii) follow-up (14-21 days after the illness onset). Model performances were evaluated using standard classification metrics and 5-fold stratified cross-validation. FINDINGS: We included PPG and clinical data from 132 adults and 15 paediatric patients with a median age of 28 (IQR, 21-35) and 12 (IQR, 9-13) years respectively. 1781 h of PPG data were available for analysis. The best performing convolutional neural network models (CNN) achieved a precision of 0.785 and recall of 0.771 in classifying adult patients according to severity class and a precision of 0.891 and recall of 0.891 in classifying between disease and post-disease state in paediatric patients. INTERPRETATION: We demonstrate that the use of a low-cost wearable provided clinically actionable data to differentiate between patients with dengue of varying severity. Continuous monitoring and connectivity to early warning systems could significantly benefit clinical care in dengue, particularly within an endemic setting. Work is currently underway to implement these models for dynamic risk predictions and assist in individualised patient care. FUNDING: EPSRC Centre for Doctoral Training in High-Performance Embedded and Distributed Systems (HiPEDS) (Grant: EP/L016796/1) and the Wellcome Trust (Grants: 215010/Z/18/Z and 215688/Z/19/Z).

Enhanced photocatalytic performance of ZnO under visible light by co-doping of Ta and C using hydrothermal method.

This study attempted to improve the photocatalytic activity of zinc oxide (ZnO) semiconductors in the visible light region by introducing the co-doping of carbon (C) and tantalum (Ta) to ZnO (ZTC) using a simple hydrothermal method with the respective precursors. The obtained uniform ZTC nanoparticles with an average crystal size of 29.30 nm (according to Scherrer's equation) revealed a redshift with a decrease in bandgap (Eg) from 3.04 eV to 2.88 eV, allowing the obtained photocatalyst to absorb the energy of the visible light for photocatalysis. Furthermore, the Zn 2p and Ta 4f core level spectra confirmed the presence of Zn2+ and Ta5+ in the ZTC sample. In addition, the infrared spectra identified hydrogen-related defects (HRDs), while the O 1s spectra indicated the existence of oxygen vacancies (VO). Electrochemical tests revealed improvement in the electron conductivity and charge separation of the obtained materials. To follow, the photocatalytic performance assessment was conducted by varying the C/Zn2+ ratios (5, 10, and 15 mol%) in ZTC samples, the initial RhB concentration (7, 15, and 30 ppm), and the pH of the RhB solution (3.0-10.0). The photodegradation on ZTC samples showed the most effectiveness for a 7 ppm RhB solution with a C/Zn2+ ratio of 10 mol% in the slightly alkaline medium (pH 9.0). Additionally, ZTC also exhibited commendable durability after being reused several times. The nature of RhB photodegradation was proposed and discussed via a mechanism at the end of this work.

Constructing B─N─P Bonds in Ultrathin Holey g-C3N4 for Regulating the Local Chemical Environment in Photocatalytic CO2 Reduction to CO.

The lack of intrinsic active sites for photocatalytic CO2 reduction reaction (CO2RR) and fast recombination rate of charge carriers are the main obstacles to achieving high photocatalytic activity. In this work, a novel phosphorus and boron binary-doped graphitic carbon nitride, highly porous material that exhibits powerful photocatalytic CO2 reduction activity, specifically toward selective CO generation, is disclosed. The coexistence of Lewis-acidic and Lewis-basic sites plays a key role in tuning the electronic structure, promoting charge distribution, extending light-harvesting ability, and promoting dissociation of excitons into active carriers. Porosity and dual dopants create local chemical environments that activate the pyridinic nitrogen atom between the phosphorus and boron atoms on the exposed surface, enabling it to function as an active site for CO2RR. The P-N-B triad is found to lower the activation barrier for reduction of CO2 by stabilizing the COOH reaction intermediate and altering the rate-determining step. As a result, CO yield increased to 22.45 µmol g-1 h-1 under visible light irradiation, which is ≈12 times larger than that of pristine graphitic carbon nitride. This study provides insights into the mechanism of charge carrier dynamics and active site determination, contributing to the understanding of the photocatalytic CO2RR mechanism.

Prevalence and determinants of constipation in children in Asia: a systematic review and meta-analysis.

Background: Constipation is prevalent worldwide, significantly increasing healthcare costs and diminishing the quality of life in children affected. Current studies have yielded mixed results regarding the factors associated with constipation, and mainly focusing on patients outside of Asia. Moreover, most of these studies lack focus on the paediatric population. This study aimed to identify the prevalence and associated factors of constipation among children in Asia. Methods: In this systematic review and meta-analysis, we systematically searched PubMed, Scopus, and Cochrane for cohort and cross-sectional studies published from database inception up to October 12, 2022, and continued with manual searching until September 2, 2023. Eligible studies were those that included children in Asia aged 0-18 years old suffering from idiopathic constipation, with prevalence value provided in the English abstract. The analysis included clinical and general population. Children with organic constipation, who had undergone gastrointestinal surgery, or with congenital defects were excluded, as these factors affect the incidence of constipation. Data included in the analysis were extracted from published reports only. The extracted data were pooled using random-effects model to analyse the prevalence of constipation in children in Asia. This study is registered with PROSPERO, CRD42022367122. Findings: Out of 4410 systematically searched studies and 36 manually searched ones, a total of 50 studies were included in the final analysis, encompassing data from 311,660 children residing in Asia. The pooled prevalence of constipation was 12.0% (95% CI 9.3-14.6%, I2 = 99.8%). There was no significant difference in constipation prevalence observed by sex and geographical location. Nonetheless, adolescents and children aged 1-9 years exhibited a significantly higher prevalence constipation compared to infants (p < 0.0001) Additionally, significant differences in constipation rates were observed across various diagnostic methods, population sources, and mental health conditions. Interpretation: Despite the high heterogeneity resulting from varying diagnostic tools or definitions used among studies, our review adds to the literature on constipation among children in Asia. It reveals a notably high prevalence of constipation in this demographic. Diagnostic methods, age, and compromised mental health emerged as significant influencers of constipation among children in Asia, highlighting potential strategies to mitigate constipation prevalence in children in Asia. Funding: The National Science and Technology Council, Taiwan.

Acute Kidney Injury After Percutaneous Coronary Intervention Guided by Intravascular Ultrasound.

Purpose We investigated the impact of intravascular ultrasound guidance on reducing the incidence of contrast-induced acute kidney injury (CI-AKI) in patients undergoing percutaneous coronary intervention (PCI). Methods Ninety-nine patients were enrolled in this prospective cohort who were not randomly assigned to angiography-guided percutaneous coronary intervention or intravascular ultrasound-guided percutaneous coronary intervention. The patients were hospitalized at the Vietnam National Heart Institute - Bach Mai Hospital between 2019 and 2020. Acute kidney injury incidence during hospitalization was the primary endpoint. Results A total of 99 patients were divided into two groups: the intravascular ultrasound-guided group (33 participants) and the angiography-guided group (66 participants). The mean ± SD contrast volume of each group was 95.2 ± 37.1 mL and 133.0 ± 36.0 mL for the ultrasound-guided and angiography-guided groups, with P < 0.0001. Intravascular imaging-guided percutaneous coronary intervention (IVUS-guided PCI) was associated with reduced acute kidney injury incidence during hospitalization: 0.0% vs. 12.12% and P = 0.049. Conclusions Intravascular ultrasound is a safe imaging tool that guides percutaneous coronary intervention and significantly reduces the rate of acute kidney injury compared to angiography alone. Patients who have a high chance of experiencing acute kidney injury benefit from using intravascular ultrasound.

Sa-TTCA: An SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing.

Accurately predicting tumor T-cell antigen (TTCA) sequences is a crucial task in the development of cancer vaccines and immunotherapies. TTCAs derived from tumor cells, are presented to immune cells (T cells) through major histocompatibility complex (MHC), via the recognition of specific portions of their structure known as epitopes. More specifically, MHC class I introduces TTCAs to T-cell receptors (TCR) which are located on the surface of CD8+ T cells. However, TTCA sequences are varied and lead to struggles in vaccine design. Recently, Machine learning (ML) models have been developed to predict TTCA sequences which could aid in fast and correct TTCA identification. During the construction of the TTCA predictor, the peptide encoding strategy is an important step. Previous studies have used biological descriptors for encoding TTCA sequences. However, there have been no studies that use natural language processing (NLP), a potential approach for this purpose. As sentences have their own words with diverse properties, biological sequences also hold unique characteristics that reflect evolutionary information, physicochemical values, and structural information. We hypothesized that NLP methods would benefit the prediction of TTCA. To develop a new identifying TTCA model, we first constructed a based model with widely used ML algorithms and extracted features from biological descriptors. Then, to improve our model performance, we added extracted features from biological language models (BLMs) based on NLP methods. Besides, we conducted feature selection by using Chi-square and Pearson Correlation Coefficient techniques. Then, SMOTE, Up-sampling, and Near-Miss were used to treat unbalanced data. Finally, we optimized Sa-TTCA by the SVM algorithm to the four most effective feature groups. The best performance of Sa-TTCA showed a competitive balanced accuracy of 87.5% on a training set, and 72.0% on an independent testing set. Our results suggest that integrating biological descriptors with natural language processing has the potential to improve the precision of predicting protein/peptide functionality, which could be beneficial for developing cancer vaccines.

Enhancing Nasopharyngeal Carcinoma Survival Prediction: Integrating Pre- and Post-Treatment MRI Radiomics with Clinical Data.

Recurrences are frequent in nasopharyngeal carcinoma (NPC) despite high remission rates with treatment, leading to considerable morbidity. This study aimed to develop a prediction model for NPC survival by harnessing both pre- and post-treatment magnetic resonance imaging (MRI) radiomics in conjunction with clinical data, focusing on 3-year progression-free survival (PFS) as the primary outcome. Our comprehensive approach involved retrospective clinical and MRI data collection of 276 eligible NPC patients from three independent hospitals (180 in the training cohort, 46 in the validation cohort, and 50 in the external cohort) who underwent MRI scans twice, once within 2 months prior to treatment and once within 10 months after treatment. From the contrast-enhanced T1-weighted images before and after treatment, 3404 radiomics features were extracted. These features were not only derived from the primary lesion but also from the adjacent lymph nodes surrounding the tumor. We conducted appropriate feature selection pipelines, followed by Cox proportional hazards models for survival analysis. Model evaluation was performed using receiver operating characteristic (ROC) analysis, the Kaplan-Meier method, and nomogram construction. Our study unveiled several crucial predictors of NPC survival, notably highlighting the synergistic combination of pre- and post-treatment data in both clinical and radiomics assessments. Our prediction model demonstrated robust performance, with an accuracy of AUCs of 0.66 (95% CI: 0.536-0.779) in the training cohort, 0.717 (95% CI: 0.536-0.883) in the testing cohort, and 0.827 (95% CI: 0.684-0.948) in validation cohort in prognosticating patient outcomes. Our study presented a novel and effective prediction model for NPC survival, leveraging both pre- and post-treatment clinical data in conjunction with MRI features. Its constructed nomogram provides potentially significant implications for NPC research, offering clinicians a valuable tool for individualized treatment planning and patient counseling.

Long-term weight gain in children with craniopharyngioma.

OBJECTIVE: Adamantinomatous craniopharyngioma mainly affects children. Excessive weight gain is a major long-term complication. The primary objective of this study was to assess long-term weight changes in children treated for craniopharyngioma. The secondary objectives were to identify risk factors for excessive weight gain and to look for associations with hypothalamic damage by the tumour or treatment. DESIGN: Single-centre retrospective cohort study. METHOD: Children managed for craniopharyngioma at our centre between 1990 and 2019 were included. The body mass index (BMI) standard deviation scores (SDS) at baseline and at last follow-up were compared. Univariate and multivariate analyses were performed in order to identify variables associated with the long-term BMI-SDS variation. RESULTS: The 108 patients had a mean follow-up of 10.4 years. The mean BMI-SDS increase over time was 2.11 (P < .001) overall, 1.21 (P < .001) in the group without hypothalamic involvement by the tumour, and 1.95 (P < .001) in the group managed using intended hypothalamus-sparing surgery. The absence of hypothalamic involvement by the tumour or treatment was significantly associated with less weight gain (P = .046 and P < .01, respectively). After adjustment, factors associated with a BMI-SDS change greater than 2 were female sex (P = .023), tumour involving the hypothalamus (P = .04), and higher baseline BMI (P < .001). CONCLUSION: Clinically significant weight gain occurred in nearly all children treated for craniopharyngioma, including those whose hypothalamus was spared by the tumour and intentionally by treatment. However, hypothalamus integrity was associated with less weight gain. Despite hypothalamus-sparing strategies, hypothalamic obesity remains a major concern, indicating a need for novel treatment approaches.

Lattice dynamics and self-trapped excitons in the Cs2SnBr6double perovskites.

Our study delved into the detailed investigation of Cs2SnBr6double perovskites, focusing on their electrical properties, lattice dynamics, and stability. The direct bandgap for Cs2SnBr6was estimated to be at 2.93 eV. One external translational mode of the Cs+lattice withT2gsymmetry and three internal modes of the octahedral withA1g,Eg, andT2gsymmetries are defined by calculated lattice dynamics, experimental micro-Raman scattering. We show a correlation with first-principles calculations, validating using a band-structured electronic approach to understanding the behavior of charge carriers, and electron-phonon interactions in Cs2SnBr6. We propose that electron-vibration interactions result in self-trapped excitons (STEs) displaying significant Stokes shifts (0.508 eV) and broad-spectrum emission. Understanding the behavior of STEs is fundamental for their optoelectronic applications.

Evaluation of Anti-Obesity and Antidiabetic Activities of Orostachys japonicus in Cell and Animal Models.

Orostachys japonicus is a popular traditional medicinal herb used in Asian countries. This study is focused on evaluating its role in lipid and glucose metabolism in cell and animal models to establish the plant as an anti-obesity and antidiabetic herb. A butanol fraction of O. japonicus was used in the study. The lipid production was evaluated by the Oil Red O technique while the expression of adipogenic markers by Western blotting and RT-PCR using 3T3-L1 preadipocyte. The effect on glucose uptake activity was evaluated in C2C12 myoblast cells. The animal study was carried out in C57BL mice to evaluate anti-obesity activity using the high-fat diet model. The evaluation of serum lipid, blood glucose, adipogenic and fibrosis markers in the liver, and fat deposition in the liver and adipose tissue (by histology) of mice was conducted. Butanol fraction of O. japonicus significantly inhibited the lipid production in the 3T3-L1 cells and reduced the expression of PPARγ, C/EBPα, SREBP-1c and aP2. It enhanced glucose uptake in insulin-resistant C2C12 myoblast cells. It reduced body weight, triglycerides, and blood glucose in the obese mice. It significantly inhibited lipid accumulation in the liver and adipose tissue of obese mice along with suppression of expression of adipogenic and fibrosis markers in the liver. In summary, supporting the previous results, this study helped to establish the potent anti-obesity, antidiabetic, and liver-protecting effect of the butanol fraction of O. japonicus.

Improving nitrogen content in soil and lemon balm (Melissa officinalis L.) yield by purple nonsulfur bacteria Rhodopseudomonas palustris in two consecutive seasons.

The study was conducted to assess the effects of nitrogen (N)-fixing purple nonsulfur bacteria (PNSB) Rhodopseudomonas palustris TLS06, VNW02, VNW64, and VNS89 on soil fertility, N uptake, essential oil (EO) content, growth, and yield of lemon balm. The experiment followed a completely randomized block design with 9 treatments and 3 replications. The treatments consisted of (i) applying 100% N as the recommended fertilizer rate (RFR), (ii) applying 85% N as RFR, (iii) applying 70% N as RFR, (iv) applying 55% N as RFR, (v) the treatment ii combined with N-PNSB, (vi) the treatment iii combined with N-PNSB, (vii) the treatment iv combined with N-PNSB, (viii) 0% as RFR combined with N-PNSB, and (ix) 0% N as RFR. The results showed that applying N-PNSB increased the plant height, and the number of primary branches in both seasons. In addition, the treatment without N fertilizer combined with N-PNSB increased stem leaf biomass by 41.2 and 50.3% in both seasons as compared with the treatment without neither N fertilizer nor N-PNSB. For soil properties, among treatments without N fertilizer, the treatment with N-PNSB increased concentrations of NH4+, soluble P, and exchangeable K+ by 41.3, 41.4, and 26.8%, respectively, as compared with the treatment without N-PNSB at the end of the second season. Applying 85% N as RFR combined with N-PNSB had a greater yield by 5.78-11.8% as compared with the treatment with 100% N as RFR, and a greater EO content by 23% as compared with the treatment with 85% N as RFR.