Exploring Uncharted Territory: Spectroscopic Evidence on a Novel Tetrel Bond with -C≡C- Triple Bond.

This study presents an investigation of the 2-butynyl alcohol···CO2 adduct, combining pulsed jet Fourier transform microwave spectroscopy with quantum chemical calculations. Two distinct isomers have been observed in the pulsed jet with a relative population ratio of 3/2. It marks the first instance of microwave spectroscopic evidence, to the best of our knowledge, suggesting the existence of a CCO2···π-C≡C- tetrel bond (π-C≡C-···π*CO2 interaction) in both observed isomers. This study highlights the importance of noncovalent interactions involving CO2 in reactant complexes, paving the way for more efficient applications of CO2 by understanding the physical basis of these noncovalent bonds.

Dynamic restoration mechanism of plant community in the burned area of northeastern margin of Qinghai-Tibet Plateau.

Forest fires play a pivotal role in influencing ecosystem evolution, exerting a profound impact on plant diversity and community stability. Understanding post-fire recovery strategies holds significant scientific importance for the ecological succession and restoration of forest ecosystems. This study utilized Partial Least Squares Path Modeling (PLS-PM) to investigate dynamic relationships among plant species diversity, phylogenetic diversity, soil properties, and community stability during various recovery stages (5-year, 15-year, and 23-year) following wildfires on the northeastern margin of the Qinghai-Tibet Plateau. The findings revealed: (1) Over time, species richness significantly decreased (p< 0.05 or p< 0.01), while species diversity and dominance increased, resulting in uniform species distribution. Community stability progressively improved, with increased species compositional similarity. (2) Throughout succession, phylogenetic diversity (PD) significantly decreased (p< 0.01), accompanied by rising Mean Pairwise Distance (MPD) and Mean Nearest Taxon Distance (MNTD). Net Relatedness Index (NRI) shifted from positive to negative, indicating an increasing aggregation and dominance of plants with similar evolutionary traits in burned areas. Early succession witnessed simultaneous environmental filtering and competitive exclusion, shifting predominantly to competitive exclusion in later stages. (3) PLS-PM revealed that in the early recovery stage, soil properties mainly affected community stability, while species diversity metamorphosed into the primary factor in the mid-to-late stages. In summary, this study showed that plant diversity and phylogenetic variation were successful in revealing changes in community structure during the succession process. Soil characteristics functioned as selective barriers for plant communities during succession, and community stability underwent a multi-faceted and dynamic process. The soil-plant dynamic feedback continuously enhanced soil conditions and community vegetation structure thereby augmenting stability. Post-fire vegetation gradually transitioned towards the original native state, demonstrating inherent ecological self-recovery capabilities in the absence of secondary disturbances.

Vitamin D deficiency and the risk of diabetic retinopathy in patients with type 2 diabetes in Tibet: a cross-sectional analysis.

BACKGROUND: Diabetic retinopathy (DR) is one of the most common complications of diabetes worldwide. The aim of this study was to assess the prevalence of DR in hospitalized patients with type 2 diabetes (T2DM) in Tibet and to identify risk factors that may influence the occurrence of DR. METHODS: This was a cross-sectional study conducted in a third-class hospital in the Tibet Autonomous Region. The prevalence of DR in hospitalized patients with T2DM was measured. Univariate and multivariate logistic regression, restricted cubic spline (RCS) analysis and receiver-operating characteristic curve analysis were used to investigate the risk factors for DR. RESULTS: The prevalence of DR was 29.3%. The duration of diabetes; concentrations of 25-OH-VitD3, hemoglobin, fasting insulin, alanine aminotransferase, total bilirubin, and creatinine; and HOMA-IR were significantly different between DR patients and non-DR patients (all P < 0.05). Univariate and multivariate logistic regression revealed that a longer duration of diabetes and lower 25-OH-VitD3 levels were associated with increased DR risk. RCS analysis suggested overall positive associations of the duration of diabetes and 25-OH-VitD3 concentrations with DR risk (P nonlinearity < 0.05). The turning points for the duration of diabetes and 25-OH-VitD3 concentrations were 5.1 years and 10.6 ng/mL, respectively. The sensitivity, specificity, and area under the receiver-operating characteristic curve for the combination of the duration of diabetes and 25-OH-VitD3 levels were 79.4%, 69.4% and 0.764, respectively. CONCLUSIONS: Given the high prevalence of DR in hospitalized patients with T2DM in Tibet, vitamin D supplementation seems to be important in the prevention of DR to some degree.

Extracellular vesicle-packaged PIAT from cancer-associated fibroblasts drives neural remodeling by mediating m5C modification in pancreatic cancer mouse models.

Perineural invasion (PNI) is a biological characteristic commonly observed in pancreatic cancer. Although PNI plays a key role in pancreatic cancer metastasis, recurrence, and poor postoperative survival, its mechanism is largely unclarified. Clinical sample analysis and endoscopic ultrasonographic elasticity scoring indicated that cancer-associated fibroblasts (CAFs) were closely related to the occurrence of PNI. Furthermore, CAF-derived extracellular vesicles (EVs) were involved in PNI in dorsal root ganglion coculture and mouse sciatic nerve models. Next, we demonstrated that CAFs promoted PNI through extracellular vesicle transmission of PNI-associated transcript (PIAT). Mechanistically, PIAT specifically bound to YBX1 and blocked the YBX1-Nedd4l interaction to inhibit YBX1 ubiquitination and degradation. Furthermore, PIAT enhanced the binding of YBX1 and PNI-associated mRNAs in a 5-methylcytosine (m5C)-dependent manner. Mutation of m5C recognition motifs in YBX1 or m5C sites in downstream target genes reversed PIAT-mediated PNI. Consistent with these findings, analyses using a KPC mouse model demonstrated that the PIAT/YBX1 axis enhanced PNI through m5C modification. Clinical data suggested that the PIAT expression in the serum EVs of patients with pancreatic cancer was associated with the degree of neural invasion and prognosis. Our study revealed the important role of the PIAT/YBX1 signaling axis in the tumor microenvironment (TME) in promoting tumor cell PNI and provided a new target for precise interference with CAFs and RNA methylation in the TME to suppress PNI in pancreatic cancer.

Design and properties of ternary alkali-activated binder based on blast furnace slag, recycled powder and waste glass powder.

Ternary alkali-activated binder was prepared by blast furnace slag (GGBS), recycled powder (RP) and waste glass powder (WGP) using simplex centroid design method. By measuring the fluidity, setting time, drying shrinkage and mechanical property of specimen, the complementary effect of GGBS, RP and WGP was discussed. The reaction mechanism and microstructure were explored by X-ray diffraction and scanning electron microscopy. The results reveal that the addition of RP could significantly reduce the fluidity and setting time of paste, while WGP can obviously improve the rheological property and play a retarding role. The workability of paste can be effectively regulated by mixing RP and WGP together. Whether added alone or in combination, RP and WGP can effectively improve the shrinkage performance. In the ternary system, GGBS can be rapidly activated and form a skeleton structure. The fine RP particles can play a good role in filling the structure, and the pozzolanic reaction of WGP gradually occurs, which makes the microstructure more compact. The incorporation of GGBS, RP and WGP can promote the growth of hydration products, improve the density of microstructure, and form a certain complementary effect.

Hypericum perforatum L. attenuates depression by regulating Akkermansia muciniphila, tryptophan metabolism and NFκB-NLRP2-Caspase1-IL1ß pathway.

BACKGROUND: Gut microbiota dysbiosis significantly contributes to progression of depression. Hypericum perforatum L. (HPL) is traditionally used in Europe for treating depression. However, its mechanism remains largely underexplored. PURPOSE: This study aims to investigate the pivotal gut microbiota species and microbial signaling metabolites associated with the antidepressant effects of HPL. METHODS: Fecal microbiota transplantation was used to assess whether HPL mitigates depression through alterations in gut microbiota. Microbiota and metabolic profiling of control, chronic restraint stress (CRS)-induced depression, and HPL-treated CRS mice were examined using 16S rRNA gene sequencing and metabolomics analysis. The influence of gut microbiota on HPL's antidepressant effects was assessed by metabolite and bacterial intervention experiments. RESULTS: HPL significantly alleviated depression symptoms in a manner dependent on gut microbiota and restored gut microbial composition by enriching Akkermansia muciniphila (AKK). Metabolomic analysis indicated that HPL regulated tryptophan metabolism, reducing kynurenine (KYN) levels derived from microbiota and increasing 5-hydroxytryptophan (5-HTP) levels. Notably, supplementation with KYN activated the NFκB-NLRP2-Caspase1-IL1ß pathway and increased proinflammatory IL1ß in the hippocampus of mice with depression. Interestingly, mono-colonization with AKK notably increased 5-hydroxytryptamine (5-HT) and decreased KYN levels, ameliorating depression symptoms through modulation of the NFκB-NLRP2-Caspase1-IL1ß pathway. CONCLUSIONS: The promising therapeutic role of HPL in treating depression is primarily attributed to its regulation of the NFκB-NLRP2-Caspase1-IL1ß pathway, specifically by targeting AKK and tryptophan metabolites.

Association between NEK1 gene polymorphisms and the potential risk of sporadic Parkinson's disease in the Chinese Northern Han population: A case-control study.

OBJECTIVE: Parkinson's disease (PD) has been identified as a genetically influenced disease linked to various genetic loci. Previous studies have suggested that neurodegenerative illnesses, including PD, Alzheimer's disease, and Amyotrophic lateral sclerosis (ALS), may share certain genetic loci. Recently, the NEK1 gene was identified as overlapping between PD and ALS. We therefore wanted to explore the potential association between the NEK1 gene single nucleotide polymorphisms (SNPs) and the clinical features and pathophysiology of sporadic PD in a northern Chinese population. METHODS: A total of 510 sporadic PD patients and 510 age- and sex-matched healthy controls (HCs) were included in this study. Two SNPs (rs4563461 and rs66509122) of the NEK1 gene were genotyped using polymerase chain reaction (PCR). And we analyzed the association between NEK1 gene polymorphisms and clinical manifestations. RESULTS: Allele T (C vs. T, P = 0.018) and genotype TT (CC vs. TT: P = 0.021) of rs66509122 among PD group and HCs were significantly different. In addition, we discovered that the rs66509122 genotype TT was associated with depression in early-onset PD (EOPD) (P = 0.031) and diabetes in female PD (P = 0.032). Unfortunately, no distinct correlation of rs4563461 polymorphisms with sporadic PD susceptibility was found in either the overall group (C vs. T, P = 0.086) or other subgroups. However, the T allele of rs4563461 was significantly correlated with sleep disorders in the PD group, especially in the late-onset PD (LOPD) group and male PD group. CONCLUSION: This study found that the NEK1 rs66509122 polymorphism was associated with a lower risk of sporadic PD, while T allele of rs66509122 may be a protective factor for PD. The NEK1 rs4563461 and rs66509122 polymorphisms both showed correlations with some non-motor symptoms in sporadic PD patients. Further research with a larger sample and varied ethnic groups is needed to investigate the role of NEK1 gene polymorphisms in the pathophysiology of PD.

Truncation mutations of CRYGD gene in congenital cataracts cause protein aggregation by disrupting the structural stability of γD-crystallin.

Congenital cataracts, a prevalent cause of blindness in children, are associated with protein aggregation. γD-crystallin, essential for sustaining lens transparency, exists as a monomer and exhibits excellent structural stability. In our cohort, we identified a nonsense mutation (c.451_452insGACT, p.Y151X) in the CRYGD gene. To explore the effect of truncation mutations on the structure of γD-crystallin, we examined the Y151X and T160RfsX8 mutations, both located in the Greek key motif 4 at the cellular and protein level in this study. Both truncation mutations induced protein misfolding and resulted in the formation of insoluble aggregates when overexpressed in HLE B3 and HEK 293T cells. Moreover, heat, UV irradiation, and oxidative stress increased the proportion of aggregates of mutants in the cells. We next purified γD-crystallin to estimate its structural changes. Truncation mutations led to conformational disruption and a concomitant decrease in protein solubility. Molecular dynamics simulations further demonstrated that partial deletion of the conserved domain within the Greek key motif 4 markedly compromised the overall stability of the protein structure. Finally, co-expression of α-crystallins facilitated the proper folding of truncated mutants and mitigated protein aggregation. In summary, the structural integrity of the Greek key motif 4 in γD-crystallin is crucial for overall structural stability.

Microbes and nutrient shift in a Closed Aquatic Ecosystem (CAES) during four weeks of operation.

A Closed Aquatic Ecosystem (CAES) housed an aquatic plant Ceratophyllum demersum, zebrafish (Danio rerio), and microbes that were simultaneously obtained with the zebrafish, and it was used to study the operation of the ecosystem. The results indicated that the CAES can operate steadily for about 4 weeks. The dissolved oxygen (DO), pH, and conductivity values of the ecosystem regularly oscillated, while the total nitrogen of the water decreased and the total phosphate slightly increased. Additionally, the chemical oxygen demand (COD, a measure of organic compounds) of the water after the experiment increased to 39 times more than that of the water before the experiment. The meta-genomic data showed that the number of genera decreased by 38 % and the top 10 most abundant genera were almost completely different before and after the experiment, which demonstrated a great shift in the microbes during the operation process. These results suggested that although the CAES operated steadily during the 28-day experiment, there were more organic materials and less nitrogen in the water by the end of the experiment, which may have influenced the structure and operation of the ecosystem. Thus, it is necessary to remove superfluous plant biomass from the CAES and supply nitrogen to keep the ecosystem stable.

Targeting the glutamine-arginine-proline metabolism axis in cancer.

Metabolic abnormalities are an important feature of tumours. The glutamine-arginine-proline axis is an important node of cancer metabolism and plays a major role in amino acid metabolism. This axis also acts as a scaffold for the synthesis of other nonessential amino acids and essential metabolites. In this paper, we briefly review (1) the glutamine addiction exhibited by tumour cells with accelerated glutamine transport and metabolism; (2) the methods regulating extracellular glutamine entry, intracellular glutamine synthesis and the fate of intracellular glutamine; (3) the glutamine, proline and arginine metabolic pathways and their interaction; and (4) the research progress in tumour therapy targeting the glutamine-arginine-proline metabolic system, with a focus on summarising the therapeutic research progress of strategies targeting of one of the key enzymes of this metabolic system, P5CS (ALDH18A1). This review provides a new basis for treatments targeting the metabolic characteristics of tumours.

Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction.

Introduction: Osteoporosis, characterized by low bone mineral density (BMD), is an increasingly serious public health issue. So far, several traditional regression models and machine learning (ML) algorithms have been proposed for predicting osteoporosis risk. However, these models have shown relatively low accuracy in clinical implementation. Recently proposed deep learning (DL) approaches, such as deep neural network (DNN), which can discover knowledge from complex hidden interactions, offer a new opportunity to improve predictive performance. In this study, we aimed to assess whether DNN can achieve a better performance in osteoporosis risk prediction. Methods: By utilizing hip BMD and extensive demographic and routine clinical data of 8,134 subjects with age more than 40 from the Louisiana Osteoporosis Study (LOS), we developed and constructed a novel DNN framework for predicting osteoporosis risk and compared its performance in osteoporosis risk prediction with four conventional ML models, namely random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and support vector machine (SVM), as well as a traditional regression model termed osteoporosis self-assessment tool (OST). Model performance was assessed by area under 'receiver operating curve' (AUC) and accuracy. Results: By using 16 discriminative variables, we observed that the DNN approach achieved the best predictive performance (AUC = 0.848) in classifying osteoporosis (hip BMD T-score ≤ -1.0) and non-osteoporosis risk (hip BMD T-score > -1.0) subjects, compared to the other approaches. Feature importance analysis showed that the top 10 most important variables identified by the DNN model were weight, age, gender, grip strength, height, beer drinking, diastolic pressure, alcohol drinking, smoke years, and economic level. Furthermore, we performed subsampling analysis to assess the effects of varying number of sample size and variables on the predictive performance of these tested models. Notably, we observed that the DNN model performed equally well (AUC = 0.846) even by utilizing only the top 10 most important variables for osteoporosis risk prediction. Meanwhile, the DNN model can still achieve a high predictive performance (AUC = 0.826) when sample size was reduced to 50% of the original dataset. Conclusion: In conclusion, we developed a novel DNN model which was considered to be an effective algorithm for early diagnosis and intervention of osteoporosis in the aging population.

Celastrol regulates the oligomeric state and chaperone activity of αB-crystallin linked with protein homeostasis in the lens.

Protein misfolding and aggregation are crucial pathogenic factors for cataracts, which are the leading cause of visual impairment worldwide. α-crystallin, as a small molecular chaperone, is involved in preventing protein misfolding and maintaining lens transparency. The chaperone activity of α-crystallin depends on its oligomeric state. Our previous work identified a natural compound, celastrol, which could regulate the oligomeric state of αB-crystallin. In this work, based on the UNcle and SEC analysis, we found that celastrol induced αB-crystallin to form large oligomers. Large oligomer formation enhanced the chaperone activity of αB-crystallin and prevented aggregation of the cataract-causing mutant ßA3-G91del. The interactions between αB-crystallin and celastrol were detected by the FRET (Fluorescence Resonance Energy Transfer) technique, and verified by molecular docking. At least 9 binding patterns were recognized, and some binding sites covered the groove structure of αB-crystallin. Interestingly, αB-R120G, a cataract-causing mutation located at the groove structure, and celastrol can decrease the aggregates of αB-R120G. Overall, our results suggested celastrol not only promoted the formation of large αB-crystallin oligomers, which enhanced its chaperone activity, but also bound to the groove structure of its α-crystallin domain to maintain its structural stability. Celastrol might serve as a chemical and pharmacological chaperone for cataract treatment.

A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture.

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to improve predictive performance. By using convolutional neural networks (CNNs) to extract features from hip DXA images, along with clinical variables, shape measurements, and texture features, our method provides a comprehensive framework for assessing fracture risk. The study cohort included 547 patients, with 94 experiencing hip fracture. A staged machine learning-based model was developed using two ensemble models: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging features). This staged approach used uncertainty quantification from Ensemble 1 to decide if DXA features are necessary for further prediction. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an AUC of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an AUC of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning. It effectively balanced accuracy and specificity, offering a robust solution when DXA data acquisition is not always feasible. Statistical tests confirmed significant differences between the models, highlighting the advantages of the advanced modeling strategies. Our staged approach offers a cost-effective holistic view of patients' health. It could identify individuals at risk with a high accuracy but reduce the unnecessary DXA scanning. Our approach has great promise to guide interventions to prevent hip fractures with reduced cost and radiation.

omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data.

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Cryptosporidium parvum disrupts intestinal epithelial barrier in neonatal mice through downregulation of cell junction molecules.

BACKGROUND: Cryptosporidium spp. cause watery diarrhea in humans and animals, especially in infants and neonates. They parasitize the apical surface of the epithelial cells in the intestinal lumen. However, the pathogenesis of Cryptosporidium-induced diarrhea is not fully understood yet. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we infected C57BL/6j neonatal mice with C. parvum IIa and IId subtypes, and examined oocyst burden, pathological changes, and intestinal epithelial permeability during the infection. In addition, transcriptomic analyses were used to study the mechanism of diarrhea induced by the C. parvum IId subtype. The neonatal mice were sensitive to both C. parvum IIa and IId infection, but the IId subtype caused a wide oocyst shedding window and maintained the high oocyst burden in the mice compared with the IIa subtype. In addition, the mice infected with C. parvum IId resulted in severe intestinal damage at the peak of infection, leading to increased permeability of the epithelial barrier. The KEGG, GO and GSEA analyses revealed that the downregulation of adherens junction and cell junction molecules at 11 dpi. Meanwhile, E-cadherin, which is associated with adherens junction, was reduced at the protein level in mouse ileum at peak and late infection. CONCLUSIONS/SIGNIFICANCE: C. parvum IId infection causes more severe pathological damage than C. parvum IIa infection in neonatal mice. Furthermore, the impairment of the epithelial barrier during C. parvum IId infection results from the downregulation of intestinal junction proteins.

Effect of plant-soil system on the restoration of community stability after wildfire in the northeast margin of Qinghai-Tibet plateau.

Wildfires, as an environmental filter, are pivotal ecological disturbances that reshape plant communities and soil dynamics, playing a crucial role in regulating biogeographic patterns and ecosystem services. In this study, we aim to explore the effects of wildfires on forest ecosystems, specifically focusing on the plant-soil feedback mechanisms within the northeastern margin of the Qinghai-Tibet Plateau (QTP). Utilizing Partial Least Squares Path Modeling (PLS-PM), we investigated the interrelationships among soil physicochemical properties, enzyme activities, species diversity, and community stability at varying post-fire recovery stages (5, 15, and 23 years). Results indicated that in the early recovery stages, rapid changes in soil properties such as decreased pH (p < 0.001) and increased nutrient availability facilitate the emergence of early successional species with high resource utilization traits. As the ecosystem evolved toward a climax community, the soil and vegetation exhibit increased stability. Furthermore, soil enzyme activities displayed dynamic patterns that corresponded with changes in soil nutrient content, directly influencing the regeneration and diversity of plant communities. Importantly, our study documented a transition in the influence of soil properties on community stability from direct positive effects in initial recovery phases to negative impacts in later stages, while indirect benefits accrue through increased species diversity and enzyme activity. Vegetation composition and structure changed dynamically with recovery time during community succession. Plant nutrient absorption and accumulation affected nutrient dynamics in the soil, influencing plant regeneration, distribution, and diversity. Our results underscore the complex interactions between soil and vegetation that drive the recovery dynamics post-wildfire, highlighting the resilience of forest ecosystems to fire disturbances. This study contributes to the understanding of post-fire recovery processes and offers valuable insights for the management and restoration of fire-affected forest ecosystems.

Benefits and Risks of Antihyperlipidemic Medication in Adults with Different Low-Density Lipoprotein Cholesterol Based on the Number Needed to Treat.

PURPOSE: The objective of this investigation is to examine the benefits and potential risks of these drugs in individuals by varying baseline low-density lipoprotein cholesterol (LDL-C) values, utilizing the concept of the number needed to treat (NNT). METHODS: We extensively searched electronic databases, such as PubMed, EMBASE, Cochrane, and Web of Science, up to 6 August 2023. Baseline LDL-C values were stratified into four categories: < 100, 100-129, 130-159, and ≥ 160 mg/dL. Risk ratios (RRs) and NNT values were computed. RESULTS: This analysis incorporated data from 46 randomized controlled trials (RCTs), encompassing a total of 237,870 participants. The meta-regression analysis demonstrated an incremental diminishing risk of major adverse cardiovascular events (MACE) with increasing baseline LDL-C values. Statins exhibited a significant reduction in MACE [number needed to treat to benefit (NNTB) 31, 95% confidence interval (CI) 25-37], but this effect was observed only in individuals with baseline LDL-C values of 100 mg/dL or higher. Ezetimibe and PCSK9 inhibitors also were effective in reducing MACE (NNTB 18, 95% CI 11-41, and NNTB 18, 95% CI 16-24). Notably, the safety outcomes of statins and ezetimibe did not reach statistical significance, while the incidence of injection-site reactions with PCSK9 inhibitors was statistically significant [number needed to treat to harm (NNTH) 41, 95% CI 80-26]. CONCLUSION: Statins, ezetimibe, and PCSK9 inhibitors demonstrated a substantial capacity to reduce MACE, particularly among individuals whose baseline LDL-C values were relatively higher. The NNT visually demonstrates the gradient between baseline LDL-C and cardiovascular disease (CVD) risk. SYSTEMATIC REVIEW REGISTRATION: Registration: PROSPERO identifier number: CRD42023458630.

Immunoactivation by Cutaneous Blue Light Irradiation Inhibits Remote Tumor Growth and Metastasis.

An improved innate immunity will respond quickly to pathogens and initiate efficient adaptive immune responses. However, up to now, there have been limited clinical ways for effective and rapid consolidation of innate immunity. Here, we report that cutaneous irradiation with blue light of 450 nm rapidly stimulates the innate immunity through cell endogenous reactive oxygen species (ROS) regulation in a noninvasive way. The iron porphyrin-containing proteins, mitochondrial cytochrome c (Cyt-c), and cytochrome p450 (CYP450) can be mobilized by blue light, which boosts electron transport and ROS production in epidermal and dermal tissues. As a messenger of innate immune activation, the increased level of ROS activates the NF-κB signaling pathway and promotes the secretion of immunomodulatory cytokines in skin. Initiated from skin, a regulatory network composed of cytokines and immune cells is established through the circulation system for innate immune activation. The innate immunity activated by whole-body blue light irradiation inhibits tumor growth and metastasis by increasing the infiltration of antitumor neutrophils and tumor-associated macrophages. Our results elucidate the remote immune modulation mechanism of blue light and provide a clinically applicable way for innate immunity activation.

[Multi-index evaluation of different parts of Amomum villosum].

To investigate the quality differences between the seeds and husks of Amomum villosum and explore the rationality of using the seeds without husks, this study determined the content of protocatechuic acid, vanillic acid, epicatechin, quercitrin, volatile oil, water extract, and ethanol extract. The 2,2-diphenyl-1-picrylhydrazyl(DPPH), 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)(ABTS), and hydroxyl radical scavenging activities were determined to evaluate the antioxidant activities of seeds and husks. The quality differences between the seeds and husks were assessed through orthogonal partial least squares-discriminant analysis(OPLS-DA) and analytic hierarchy process(AHP) combined with the entropy weight method(EWM). Significant differences(P<0.05) were observed in all 10 indicators between the seeds and husks. The levels of epicatechin, quercetin, and volatile oil were higher in the seeds, whereas those of protocatechuic acid, vanillic acid, water extract, and ethanol extract were higher in the husks. The seeds showed stronger scavenging ability against DPPH and ABTS radicals, while the husks showed a stronger scavenging effect on hydroxyl radicals. OPLS-DA significantly discriminated between the seeds and husks. Furthermore, volatile oil, water extract, DPPH radical scavenging rate, quercitrin, ABTS radical scavenging rate, hydroxyl radical scavenging rate, and vanillic acid were selected as the main differential indicators by variable importance in projection(VIP). Comprehensive scores calculated by AHP combined with EWM indicated that the seeds were superior to husks in terms of overall quality. However, there are still some dominant components and a certain antioxidant effect in the husks. Therefore, it is suggested to using Amomi Fructus with a certain amount of husks or utilizing the husks for other purposes.