Identification of potential therapeutic targets for plaque vulnerability based on an integrated analysis.

BACKGROUND AND AIMS: This study aimed to explore potential hub genes and pathways of plaque vulnerability and to investigate possible therapeutic targets for acute coronary syndrome (ACS). METHODS AND RESULTS: Four microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene coexpression networks (WGCNA) and immune cell infiltration analysis (IIA) were used to identify the genes for plaque vulnerability. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology, Gene Ontology annotation and protein-protein interaction (PPI) network analyses were performed to explore the hub genes. Random forest and artificial neural networks were constructed for validation. Furthermore, the CMap and Herb databases were employed to explore possible therapeutic targets. A total of 168 DEGs with an adjusted P < 0.05 and approximately 1974 IIA genes were identified in GSE62646. Three modules were detected and associated with CAD-Class, including 891 genes that can be found in GSE90074. After removing duplicates, 114 hub genes were used for functional analysis. GO functions identified 157 items, and 6 pathways were enriched for the KEGG pathway at adjusted P < 0.05 (false discovery rate, FDR set at < 0.05). Random forest and artificial neural network models were built based on the GSE48060 and GSE34822 datasets, respectively, to validate the previous hub genes. Five genes (GZMA, GZMB, KLRB1, KLRD1 and TRPM6) were selected, and only two of them (GZMA and GZMB) were screened as therapeutic targets in the CMap and Herb databases. CONCLUSION: We performed a comprehensive analysis and validated GZMA and GZMB as a target for plaque vulnerability, which provides a therapeutic strategy for the prevention of ACS. However, whether it can be used as a predictor in blood samples requires further experimental verification.

Adolescent suicide risk factors and the integration of social-emotional skills in school-based prevention programs.

Adolescents are considered one of the most vulnerable groups affected by suicide. Rapid changes in adolescents' physical and mental states, as well as in their lives, significantly and undeniably increase the risk of suicide. Psychological, social, family, individual, and environmental factors are important risk factors for suicidal behavior among teenagers and may contribute to suicide risk through various direct, indirect, or combined pathways. Social-emotional learning is considered a powerful intervention measure for addressing the crisis of adolescent suicide. When deliberately cultivated, fostered, and enhanced, self-awareness, self-management, social awareness, interpersonal skills, and responsible decision-making, as the five core competencies of social-emotional learning, can be used to effectively target various risk factors for adolescent suicide and provide necessary mental and interpersonal support. Among numerous suicide intervention methods, school-based interventions based on social-emotional competence have shown great potential in preventing and addressing suicide risk factors in adolescents. The characteristics of school-based interventions based on social-emotional competence, including their appropriateness, necessity, cost-effectiveness, comprehensiveness, and effectiveness, make these interventions an important means of addressing the crisis of adolescent suicide. To further determine the potential of school-based interventions based on social-emotional competence and better address the issue of adolescent suicide, additional financial support should be provided, the combination of social-emotional learning and other suicide prevention programs within schools should be fully leveraged, and cooperation between schools and families, society, and other environments should be maximized. These efforts should be considered future research directions.

Potential use of large language models for mitigating students' problematic social media use: ChatGPT as an example.

The problematic use of social media has numerous negative impacts on individuals' daily lives, interpersonal relationships, physical and mental health, and more. Currently, there are few methods and tools to alleviate problematic social media, and their potential is yet to be fully realized. Emerging large language models (LLMs) are becoming increasingly popular for providing information and assistance to people and are being applied in many aspects of life. In mitigating problematic social media use, LLMs such as ChatGPT can play a positive role by serving as conversational partners and outlets for users, providing personalized information and resources, monitoring and intervening in problematic social media use, and more. In this process, we should recognize both the enormous potential and endless possibilities of LLMs such as ChatGPT, leveraging their advantages to better address problematic social media use, while also acknowledging the limitations and potential pitfalls of ChatGPT technology, such as errors, limitations in issue resolution, privacy and security concerns, and potential overreliance. When we leverage the advantages of LLMs to address issues in social media usage, we must adopt a cautious and ethical approach, being vigilant of the potential adverse effects that LLMs may have in addressing problematic social media use to better harness technology to serve individuals and society.

Climate change, ambient air pollution, and students' mental health.

The impact of global climate change and air pollution on mental health has become a crucial public health issue. Increased public awareness of health, advancements in medical diagnosis and treatment, the way media outlets report environmental changes and the variation in social resources affect psychological responses and adaptation methods to climate change and air pollution. In the context of climate change, extreme weather events seriously disrupt people's living environments, and unstable educational environments lead to an increase in mental health issues for students. Air pollution affects students' mental health by increasing the incidence of diseases while decreasing contact with nature, leading to problems such as anxiety, depression, and decreased cognitive function. We call for joint efforts to reduce pollutant emissions at the source, improve energy structures, strengthen environmental monitoring and gover-nance, increase attention to the mental health issues of students, and help student groups build resilience; by establishing public policies, enhancing social support and adjusting lifestyles and habits, we can help students cope with the constantly changing environment and maintain a good level of mental health. Through these comprehensive measures, we can more effectively address the challenges of global climate change and air pollution and promote the achievement of the United Nations Sustainable Development Goals.

Using ChatGPT to promote college students' participation in physical activities and its effect on mental health.

As one of the most famous large language models, ChatGPT has great potential for application in physical education. It can provide personalized exercise plans, a variety of exercise options, and interactive support. The integration of ChatGPT into the teaching process can promote college students' participation in physical activities and improve their mental health while expanding the traditional teaching environment and promoting the reform of traditional teaching methods. However, the application of ChatGPT faces challenges and obstacles in physical education. To make full use of ChatGPT in physical education, it can be combined with wearable devices and sports equipment to enhance the efficiency of interactions with users. Relevant policies are urgently needed to avoid the improper use of users' data.

Unlocking the power of physical activity in easing psychological distress.

The severity of the current global mental health situation and the importance of maintaining psychological well-being call for more powerful, convenient, and efficient solutions for addressing psychological issues and relieving mental stress. Physical activity not only effectively improves physical fitness and reduces negative emotions such as anxiety and depression but also increases the improvement of psychological health and sense of well-being. At the same time, physical activity interventions for mental health have unique advantages, including reducing the side effects of psychological interventions and increasing necessity, convenience, and cost-effectiveness, as well as flexible adaptability across multiple methods, groups, and age ranges, providing stronger support for relieving psychological stress and addressing psychological issues. Although physical activity is an important intervention measure in relieving psychological stress, its value and role in mental health care seem to have not yet received sufficient attention, and its potential remains to be further revealed. Given the significant advantages and effectiveness of physical activity in mental health intervention practices, it is necessary to stimulate its potential in relieving psychological stress through various means in future studies to better safeguard the public's physical and mental health. Developing guidelines for physical activity for improved mental health, enhancing organic integration with other inter-vention measures, and providing necessary respect, encouragement, and support are important directions to consider.

Potential and limitations of ChatGPT and generative artificial intelligence in medical safety education.

The primary objectives of medical safety education are to provide the public with essential knowledge about medications and to foster a scientific approach to drug usage. The era of using artificial intelligence to revolutionize medical safety education has already dawned, and ChatGPT and other generative artificial intelligence models have immense potential in this domain. Notably, they offer a wealth of knowledge, anonymity, continuous availability, and personalized services. However, the practical implementation of generative artificial intelligence models such as ChatGPT in medical safety education still faces several challenges, including concerns about the accuracy of information, legal responsibilities, and ethical obligations. Moving forward, it is crucial to intelligently upgrade ChatGPT by leveraging the strengths of existing medical practices. This task involves further integrating the model with real-life scenarios and proactively addressing ethical and security issues with the ultimate goal of providing the public with comprehensive, convenient, efficient, and personalized medical services.

Appraising the causal relationship between thyroid function and rheumatoid arthritis: a two-sample bidirectional Mendelian randomization study.

Background: Hypothyroidism and hyperthyroidism are observationally associated with rheumatoid arthritis (RA), but causality is unclear. To evaluate the causal relationship between thyroid function and RA, we conducted a two-Sample bidirectional Mendelian Randomization (MR) study. Methods: Single nucleotide polymorphisms associated with six phenotypes were selected from the FinnGen biobank database, The ThyroidOmics Consortium database, and the IEU Open GWAS database. For the forward MR analysis, we selected hypothyroidism (N=213,390), Graves' disease (GD) (N=199,034), other types of hyperthyroidism (N=190,799), free thyroxine (FT4, N=49,269), and thyroid-stimulating hormone (TSH, N=54,288) as the five related thyroid function phenotypes for exposure, with RA (N=58,284) as the outcome. Reverse MR analysis selected RA as the exposure and five phenotypes of thyroid function as the outcome. The Inverse variance weighting (IVW) method was used as the primary analysis method, supplemented by weighted median (WM) and MR-Egger methods. Cochran's Q test, MR-PRESSO, MR-Egger regression methods, and leave-one-out analysis were employed to assess sensitivity and pleiotropy. Results: Forward MR evidence indicates that genetic susceptibility to hypothyroidism is associated with an increased risk of RA (ORIvw=1.758, P=7.61×10-5). Reverse MR evidence suggests that genetic susceptibility to RA is associated with an increased risk of hypothyroidism (ORIvw=1.274, P=3.88×10-20), GD (ORIvw=1.269, P=8.15×10-05), and other types of hyperthyroidism (ORIvw=1.141, P=1.80×10-03). There is no evidence to support a forward or reverse causal relationship between genetic susceptibility to RA and FT4, TSH. Conclusion: Our results provide genetic evidence supporting bidirectional causal relationships between thyroid function and RA. These findings inform preventive strategies and interventions targeting RA and thyroid dysfunction.

Pyramiding Breeding of Low-Glutelin-Content Indica Rice with Good Quality and Resistance.

Low-glutelin-content rice, a type of functional rice with glutelin levels below 4%, is an essential dietary supplement for chronic kidney disease (CKD) patients. Developing low-glutelin-content rice varieties is crucial to catering to the growing CKD population. In this study, we aimed to create a new low-glutelin indica rice variety with excellent agronomic traits. To achieve this, we employed a combination of molecular-marker-assisted selection and traditional breeding techniques. The cultivars W3660, Wushansimiao (WSSM), and Nantaixiangzhan (NTXZ) were crossbred, incorporating the Lgc-1, Pi-2, Xa23, and fgr alleles into a single line. The result of this breeding effort was "Yishenxiangsimiao", a new indica rice variety that inherits the desirable characteristics of its parent lines. Yishenxiangsimiao (YSXSM) possesses not only a low glutelin content but also dual resistance to blast and bacterial blight (BB). It exhibits high-quality grains with a fragrant aroma. This new low-glutelin indica cultivar not only ensures a stable food supply for CKD patients but also serves as a healthy dietary option for the general public. We also performed RNA-seq of these rice varieties to investigate their internal gene expression differences. The YSXSM exhibited a higher biotic-resistance gene expression in comparison to NTXZ. In summary, we successfully developed a novel low-glutelin indica rice variety, "Yishenxiangsimiao", with superior agronomic traits. This rice variety addresses the dietary needs of CKD patients and offers a nutritious choice for all consumers.

The Dsup coordinates grain development and abiotic stress in rice.

DNA damage is a serious threat to all living organisms and may be induced by environmental stressors. Previous studies have revealed that the tardigrade (Ramazzotius varieornatus) DNA damage suppressor protein Dsup has protective effects in human cells and tobacco. However, whether Dsup provides radiation damage protection more widely in crops is unclear. To explore the effects of Dsup in other crops, stable Dsup overexpression lines through Agrobacterium-mediated transformation were generated and their agronomic traits were deeply investigated. In this study, the overexpression of Dsup not only enhanced the DNA damage resistance at the seeds and seedlings stages, they also exhibited grain size enlargement and starch granule structure and cell size alteration by the scanning electron microscopy observation. Notably, the RNA-seq revealed that the Dsup plants increased radiation-related and abiotic stress-related gene expression in comparison to wild types, suggesting that Dsup is capable to coordinate normal growth and abiotic stress resistance in rice. Immunoprecipitation enrichment with liquid chromatography-tandem mass spectrometry (IP-LC-MS) assays uncovered 21 proteins preferably interacting with Dsup in plants, suggesting that Dsup binds to transcription and translation related proteins to regulate the homeostasis between DNA protection and plant development. In conclusion, our data provide a detailed agronomic analysis of Dsup plants and potential mechanisms of Dsup function in crops. Our findings provide novel insights for the breeding of crop radiation resistance.

The METS-IR is independently related to bone mineral density, FRAX score, and bone fracture among U.S. non-diabetic adults: a cross-sectional study based on NHANES.

AIM: The purpose of this study was to investigate the association between the metabolic score for insulin resistance (METS-IR) and bone mineral density (BMD) in American non-diabetic adults. METHODS: We conducted a cross-sectional study with 1114 non-diabetic adults from the National Health and Nutrition Examination Survey cycle (2013-2014). The associations between METS-IR and BMD of total femur and spine were assessed by the multiple linear regression and verified the non-linear relationship with a smooth curve fit and threshold effect model. Furthermore, we evaluated the relationship between METS-IR, FRAX score, and history of bone fractures. RESULTS: We found that BMD of the total femur and spine increased by 0.005 g/cm3 and 0.005 g/cm3, respectively, for a one-unit increase of METS-IR in all participants. This positive association was more pronounced among higher METS-IR participants, and there was a non-linear relationship, which was more significant when the MTTS-IRfemur was < 41.62 or the METS-IRspine was < 41.39 (ßfemur = 0.008, ßspine = 0.011, all P < 0.05). We also found that METS-IR was positively correlated with both FRAX scores in all female participants. However, METS-IR was positively correlated only with the 10-year hip fracture risk score in male participants with fractures. No significant association between METS-IR and a history of bone fractures. CONCLUSIONS: In American non-diabetic adults, there is a correlation between elevated levels of METS-IR within the lower range and increased BMD as well as decreased risk of fractures, suggesting that METS-IR holds promise as a novel biomarker for guiding osteoporosis (OP) prevention. However, it is important to carefully balance the potential benefits and risks of METS-IR in OP.

[Detection and analysis of moving reaction boundary-based electrophoresis distance using smartphone images].

Electrophoresis titration (ET) based on the moving reaction boundary (MRB) theory can detect the analyte contents in different samples by converting content signals into distance signals. However, this technique is only suitable for on-site qualitative testing, and accurate quantification relies on complex optical equipment and computers. Hence, applying this method to real-time point-of-care testing (POCT) is challenging. In this study, we developed a smartphone-based ET system based on a visual technique to achieve real-time quantitative detection. First, we developed a portable quantitative ET device that can connect to a smartphone; this device consisted of five components, namely, an ET chip, a power module, a microcontroller, a liquid crystal display screen, and a Bluetooth module. The device measured 10 cm×15 cm×2.5 cm, weighed 300 g, and was easy to hold. Thus, it is suitable for on-site testing with a run time of only 2-4 min. An assistant mobile software program was also developed to control the device and perform ET. The colored electrophoresis boundary can be captured using the smartphone camera, and quantitative detection results can be obtained in real time. Second, we proposed a quantitative algorithm based on ET channels. The software was used to recognize the boundary migration distance of three channels, a standard curve based on two given contents of the standards was established using the two-point method, and the content of the test sample was calculated. Human serum albumin (HSA) and uric acid (UA) were used as a model protein and biosample, respectively, to test the performance of the detection system. For HSA detection, different HSA solutions were mixed with a polyacrylamide gel (PAG) stock solution, phenolphthalein was added as an indicator, and sodium persulfate and tetramethyl ethylenediamine (TEMED) were used to promote polymerization to form a gel. For UA detection, agarose gel was filled into the ET channel, the UA sample, urate oxidase, and leucomalachite green were added into the anode cell and incubated for 20 min. ET was then performed. The fitting goodness (R2) values of HSA and UA were 0.9959 and 0.9935, respectively, with a linear range of 0.5-35.0 g/L and a log-linear range of 100-4000 µmol/L. The limits of detection for HSA and UA were 0.05 g/L and 50 µmol/L, respectively, and the corresponding relative standard deviations (RSDs) were not greater than 2.87% and 3.21%, respectively. These results demonstrate that the detection system has good accuracy and sensitivity. Clinical samples collected from healthy volunteers were used as target blood samples, and the developed system was used to measure serum total protein and UA levels. Serum samples from five volunteers were selected, standard curves of total serum protein and UA were established, and the test results were compared with hospital standard testing results. The relative errors for serum total protein and UA were less than 6.03% and 6.21%, respectively, and the corresponding RSDs were less than 3.72% and 5.84%, respectively. These findings verify the accuracy and reliability of the proposed detection system. The smartphone-based ET detection system introduced in this paper presents several advantages. First, it enables the portable real-time detection of total serum protein and UA. Second, compared with traditional ET strategies based on colored boundaries, it does not rely on optical detection equipment or computers to obtain quantitative detection results; as such, it can reduce the complexity of the operation and provide portability and real-time metrics. Third, the detection of two biomarkers, serum total protein and UA, is achieved on the same device, thereby improving the multitarget detection potential of the ET method. These advantages render the developed method a promising detection platform for clinical applications and real-time POCT.

Appraising causal risk and protective factors for rheumatoid arthritis.

Aims: Mendelian randomization (MR) is considered to overcome the bias of observational studies, but there is no current meta-analysis of MR studies on rheumatoid arthritis (RA). The purpose of this study was to summarize the relationship between potential pathogenic factors and RA risk based on existing MR studies. Methods: PubMed, Web of Science, and Embase were searched for MR studies on influencing factors in relation to RA up to October 2022. Meta-analyses of MR studies assessing correlations between various potential pathogenic factors and RA were conducted. Random-effect and fixed-effect models were used to synthesize the odds ratios of various pathogenic factors and RA. The quality of the study was assessed using the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) guidelines. Results: A total of 517 potentially relevant articles were screened, 35 studies were included in the systematic review, and 19 studies were eligible to be included in the meta-analysis. Pooled estimates of 19 included studies (causality between 15 different risk factors and RA) revealed that obesity, smoking, coffee intake, lower education attainment, and Graves' disease (GD) were related to the increased risk of RA. In contrast, the causality contribution from serum mineral levels (calcium, iron, copper, zinc, magnesium, selenium), alcohol intake, and chronic periodontitis to RA is not significant. Conclusion: Obesity, smoking, education attainment, and GD have real causal effects on the occurrence and development of RA. These results may provide insights into the genetic susceptibility and potential biological pathways of RA.

Delivering substance use prevention interventions for adolescents in educational settings: A scoping review.

Currently, a proportion of adolescents use alcohol, tobacco, and illicit drugs, which inevitably harms their health and academic progress. Adolescence is a peak period for substance use initiation and a critical time for preventing substance use problems. Various entities, such as families, schools, and communities, have implemented a variety of interventions to alleviate adolescent substance use problems, and schools play a unique role. To explore the types, characteristics, and effectiveness of substance use interventions in educational settings for adole-scents, we conducted a scoping review and identified 32 studies after screening. We divided the 32 studies according to intervention type, including curriculum interventions focusing on cognitive-behavioral skill enhancement, exercise interventions, peer interventions and family-school cooperation, and electronic interventions. Except for the mixed results on electronic interventions, the results showed that the other interventions were beneficial to different extents in alleviating adolescent substance use problems. In addition, we analyzed and summarized the advantages and challenges of intervening in adolescent substance use in educational settings. Schools can use equipment and human resources to provide adolescents with various types of intervention measures, but they also face challenges such as stigmatization, ineffective coordination among multiple resources, and poor implementation effects. In the future, school-based intervention measures can fully utilize big data and artificial intelligence technology and collaborate with families and communities to intervene appro-priately while paying attention to the comorbidity risks of substance use disorders and psychological health issues.

Bidirectional two-sample mendelian randomization analysis identifies causal associations between age at menarche and sarcopenia-related traits.

BACKGROUND: Observational studies have shown that the age of menarche is associated with sarcopenia, but confounding factors make the causal relationship difficult to infer. OBJECTIVE: Therefore, we conducted a bidirectional two-sample Mendelian randomized (MR) analysis to evaluate the potential causal relationship between age at menarche and sarcopenia-related traits (hand grip strength, lean mass, walking pace). METHODS: We obtained the latest aggregate statistics from the Genome-wide association studies (GWAS) database on the age of menarche of 182,416 participants from ReproGen, the appendicular lean mass of 244,730 participants from EMBL's European Bioinformatics Institute, the left-hand grip strength of 401,026 participants, the right-hand grip strength of 461,089 participants and the usual walking pace of 459,915 participants from the UK Biobank. The inverse variance weighting (IVW) method and other MR methods were used to evaluate the bidirectional causal relationship between the age of menarche and sarcopenia. RESULTS: The forward MR results showed that the age of menarche predicted by the gene was positively correlated with left-hand grip strength (IVWß=0.041, P = 2.00 × 10-10), right-hand grip strength (IVWß=0.053, P = 1.97 × 10-18), appendicular lean mass (IVWß=0.012, P = 4.38 × 10-13) and usual walking pace (IVWß=0.033, P = 1.62 × 10-8).In the reverse MR analysis, we also found that the usual walking pace was positively correlated with the age of menarche predicted by genes (IVWß=0.532, P = 1.65 × 10-4). Still, there was no causal relationship between grip strength and appendicular lean mass and the age at menarche. CONCLUSION: Our results show that earlier menarche will increase the risk of sarcopenia. In addition, people with higher muscle function tend to have menarche later. These findings may provide a reference for prevention strategies and interventions for menarche in advance and sarcopenia.

Relationship between depression, smartphone addiction, and sleep among Chinese engineering students during the COVID-19 pandemic.

BACKGROUND: Existing research has demonstrated that depression is positively related to smartphone addiction, but the role of sleep has not been discussed thoroughly, especially among engineering undergraduates affected by the coronavirus disease 2019 pandemic. AIM: To evaluate sleep as a mediator of the association between smartphone addiction and depression among engineering undergraduates. METHODS: Using a multistage stratified random sampling method, a cross-sectional survey was conducted among 692 engineering undergraduates from a top engineering university in China, and data were collected by self-reported electronic questionnaires. The data included demographic characteristics, such as age, gender, the Smartphone Addiction Scale-Short Version (SAS-SV), the 9-item Patient Health Questionnaire, and the Pittsburgh Sleep Quality Index. Pearson correlation and multiple linear regression analyses were used to examine the association between smartphone addiction and depression, while structural equation models were established to evaluate the possible mediating role of sleep. RESULTS: Based on the cutoffs of the SAS-SV, the rate of smartphone addiction was 63.58 percent, with 56.21 percent for women and 65.68 percent for men, among 692 engineering students. The prevalence of depression among students was 14.16 percent, with 17.65 percent for women, and 13.18 percent for men. Smartphone addiction was positively correlated with depression, and sleep played a significant mediating effect between the two, accounting for 42.22 percent of the total effect. In addition, sleep latency, sleep disturbances, and daytime dysfunction significantly mediated the relationship between depression and smartphone addiction. The mediating effect of sleep latency was 0.014 [P < 0.01; 95% confidence interval (CI): 0.006-0.027], the mediating effect of sleep disturbances was 0.022 (P < 0.01; 95%CI: 0.011-0.040), and the mediating effect of daytime dysfunction was 0.040 (P < 0.01; 95%CI: 0.024-0.059). The influence of sleep latency, sleep disturbances, and daytime dysfunction accounted for 18.42%, 28.95%, and 52.63% of the total mediating effect, respectively. CONCLUSION: The results of the study suggest that reducing excessive smartphone use and improving sleep quality can help alleviate depression.

Digital interventions empowering mental health reconstruction among students after the COVID-19 pandemic.

With the gradual end of the coronavirus disease 2019 (COVID-19) pandemic, the reconstruction of students' mental health is urgently necessary. Digital interventions offer advantages such as high accessibility, anonymity, and accurate identification, which can promote the reconstruction of students' mental health through the provision of psychological support platforms, psychological assessment tools, and online mental health activities. However, we recognize that digital interventions must undergo many adjustments, and corresponding ethical norms require further clarification. It is crucial for different stakeholders to collaborate and work toward maximizing the effectiveness of digital interventions for the reconstruction of mental health after the COVID-19 pandemic.

Causal effects of tea intake on multiple types of fractures: A two-sample Mendelian randomization study.

Fracture is a global public health disease. Bone health and fracture risk have become the focus of public and scientific attention. Observational studies have reported that tea consumption is associated with fracture risk, but the results are inconsistent. The present study used 2-sample Mendelian randomization (MR) analysis. The inverse variance weighted method, employing genetic data from UK Biobank (447,485 cases) of tea intake and UK Biobank (Genome-wide association study Round 2) project (361,194 cases) of fractures, was performed to estimate the causal relationship between tea intake and multiple types of fractures. The inverse variance weighted indicated no causal effects of tea consumption on fractures of the skull and face, shoulder and upper arm, hand and wrist, femur, calf, and ankle (odds ratio = 1.000, 1.000, 1.002, 0.997, 0.998; P = .881, 0.857, 0.339, 0.054, 0.569, respectively). Consistent results were also found in MR-Egger, weighted median, and weighted mode. Our research provided evidence that tea consumption is unlikely to affect the incidence of fractures.

The metabolic score of insulin resistance is positively correlated with bone mineral density in postmenopausal patients with type 2 diabetes mellitus.

The prevalence of type 2 diabetes mellitus (T2DM) complicated with osteoporosis (OP) is increasing yearly. Early prevention, detection and treatment of OP are important in postmenopausal patients with T2DM. This study aimed to explore the correlation between insulin resistance and bone mineral density (BMD), and OP in postmenopausal patients with T2DM. In this study, postmenopausal patients with T2DM who visited our hospital from January 2021 to March 2022 were divided into the OP group (n = 91) and non-OP group (n = 119) according to whether they were complicated with OP or not. The general data of patients, BMD, blood routine, glucose metabolism, lipid metabolism, liver and kidney function indexes were collected, and the homeostatic model assessment for IR (HOMA-IR), the triglyceride-glucose (TyG) index and the metabolic score for IR (METS-IR) were calculated. A weighted multivariate linear regression model assessed the correlation between insulin resistance (IR) related indexes and lumbar spine, femoral neck, and hip BMD. A weighted logistic regression model assessed the odds ratios (ORs) and 95% confidence intervals (95% CIs) for the association between the IR-related indexes and OP risk. The nonlinear relationship was also evaluated by smooth curve fitting (SCF) and a weighted generalized additive model (GAM). Moreover, the Receiver-operating characteristics (ROC) curve was used to analyze the predictive efficiency of METS-IR in postmenopausal patients with T2DM with OP. HOMA-IR, TyG, and METS-IR in the OP group were lower than those in the non-OP group (all P < 0.05). Weighted multiple linear regression after adjusting covariates showed that METS-IR was positively correlated with the lumbar spine, femoral neck, and hip BMD (ßMETS-IR = 0.006,0.005,0.005, all P < 0.001). The results of weighted Logistic regression and GAM showed that when METS-IR < 44.5, each unit of increased METS-IR value was associated with a decreased OP risk of 12% (P = 0.002). When METS-IR ≥ 44.5, there was no significant correlation between METS-IR and the risk of OP (OR = 1.00, P = 0.934). Similar trends were not observed in HOMA-IR and TyG. The ROC suggested helpful discriminative power of the METS-IR index for T2DM. We confirmed that METS-IR, as a novel alternative marker of IR, had a positive association with BMD in postmenopausal patients with T2DM, and METS-IR was a protective factor for OP in a specific range.

Global boundedness of a higher-dimensional chemotaxis system on alopecia areata.

This paper mainly focuses on the dynamics behavior of a three-component chemotaxis system on alopecia areata $ \begin{equation*} \left\{ \begin{array}{lll} u_t = \Delta{u}-\chi_1\nabla\cdot(u\nabla{w})+w-\mu_1u^2, &x\in\Omega, t>0, \\ v_t = \Delta{v}-\chi_2\nabla\cdot(v\nabla{w})+w+ruv-\mu_2v^2, &x\in \Omega, t>0, \\ w_t = \Delta{w}+u+v-w, &x\in \Omega, t>0, \\ \frac{\partial{u}}{\partial{\nu}} = \frac{\partial{v}}{\partial{\nu}} = \frac{\partial{w}}{\partial{\nu}} = 0, &x\in \partial \Omega, t>0, \\ u(x, 0) = u_0(x), \ v(x, 0) = v_0(x), \ w(x, 0) = w_0(x), &x\in \Omega, \ \end{array} \right. \end{equation*} $ where $ \Omega\subset\mathbb{R}^n $ $ (n \geq 4) $ is a bounded convex domain with smooth boundary $ \partial\Omega $, the parameters $ \chi_i $, $ \mu_i $ $ (i = 1, 2) $, and $ r $ are positive. We show that this system exists a globally bounded classical solution if $ \mu_i\; (i = 1, 2) $ is large enough. This result extends the corresponding results which were obtained by Lou and Tao (JDE, 2021) to the higher-dimensional case.