Independent Analysis Research Articles

Investigating the organizational commitment and its associated factors among the staff of the health sector: a cross-sectional research

IntroductionOrganizational commitment (OC) significantly impacts the quality of care provided by healthcare staff. It reflects employees’ connection to and engagement with their organization, affecting job satisfaction, employee turnover, and the overall success of the healthcare organization. This research seeks to assess OC levels and identify its influencing factors among health sector staff in Shiraz, southwestern Iran.MethodsIn this cross-sectional study, the staff of the health sector including physicians/family physician (FP), midwives, healthcare workers, psychologists, dentists, and nutritionists were selected through a multi-stage sampling; they filled out an online 36-item questionnaire (12 demographic items and 24-item Allen and Mayer OC questionnaire). The data were analyzed using SPSS software (version 25). Descriptive statistics of OC scores were provided. Also, independent samples t-test and analysis of variance were used as statistical tests, and P-value < 0.05 was considered the significant level.ResultsOverall, 289 staff with a mean age of 32 ± 7.6 years participated in this study. The OC total mean score was higher in the public sector compared with the private sector and higher in the non-FP team compared with the FP team. The mean score of all types of OC among all job positions was low. Moreover, education level, workplace, job experience in current job, engagement in other jobs for making money, and FP staff showed a significant association with the total mean score of OC.ConclusionAccording to the results, OC among health sector staff is low. Therefore, policymakers in the health sector are strongly recommended to implement immediate measures to enhance this critical factor among their workforce.

Lecturers’ Awareness of Artificial Intelligence Tools for Teaching and Research in Alvan Ikoku Federal University of Education, Nigeria

Technology integration into curriculum no doubt has so many benefits that lecturers in tertiary educational institutions can derive. This study examined lecturers’ awareness of Artificial Intelligence (AI) tools for teaching and research at Alvan Ikoku Federal University of Education Owerri Nigeria. The AI tools examined in the study include ChatGPT, PowerPoint Speaker Coach Quillbot, Perplexity, Scholarcy, Gradescope, Mendeley Gemini, MATHia, ChatPDF, Consensus, Research Rabbit, Class Point and Scite. Three research questions and two research hypotheses guided the study. The population of study comprised lecturers in the university. Data for the study was collected using researchers developed questionnaire tagged Lecturers’ Awareness of Artificial Intelligence Tools for Teaching and Research Questionnaire (LAAITTRQ) on WhatsApp. Mean and standard deviation were used to answer research questions while independent samples t-test statistics and Analysis of Variance (ANOVA) test were employed to test the formulated hypotheses at a 0.05 level of significance. Findings reveal that lecturers are aware of AI tools for teaching and research. Furthermore, it was discovered that there was no significant difference in awareness according to gender. However, significant differences in awareness exist according to teaching experience with the least experienced lecturers having awareness mean scores higher than the most experienced and the moderately experienced. Based on the findings, the researchers recommended that workshops and training on the use of AI tools in teaching and research should be organized in the university to boost their knowledge and skills among others.

Exploring replica-Potts CFTs in two dimensions

We initiate a numerical conformal bootstrap study of CFTs with Sn ⋉ (SQ)n global symmetry. These include CFTs that can be obtained as coupled replicas of two-dimensional critical Potts models. Particular attention is paid to the special case S3 ⋉ (S3)3, which governs the critical behaviour of three coupled critical 3-state Potts models, a multi-scalar realisation of a (potentially) non-integrable CFT in two dimensions. The model has been studied in earlier works using perturbation theory, transfer matrices, and Monte Carlo simulations. This work represents an independent non-perturbative analysis. Our results are in agreement with previous determinations: we obtain an allowed peninsula within parameter space for the scaling dimensions of the three lowest-lying operators in the theory, which contains the earlier predictions for these scaling dimensions. Additionally, we derive numerous bounds on admissible scaling dimensions in the theory, which are compatible with earlier results. Our work sets the necessary groundwork for a future precision study of these theories in the conformal bootstrap.

A fronto-insular network underlies individual variations in anger expression and control

Abstract Anger can be deconstructed into distinct components: a tendency to outwardly express it (anger-out) and the capability to manage it (anger control). These aspects exhibit individual differences that vary across a continuum. Notably, the capacity to express and control anger is of great importance to modulate our reactions in interpersonal situations. The aim of this study was to test the hypothesis that anger expression and control are negatively correlated and that both can be decoded by the same patterns of grey and white matter features of a fronto-temporal brain network. To this aim, a data fusion unsupervised machine learning technique, known as transposed Independent Vector Analysis (tIVA), was used to decompose the brain into covarying GM–WM networks and then backward regression was used to predict both anger expression and control from a sample of 212 healthy subjects. Confirming our hypothesis, results showed that anger control and anger expression are negatively correlated, the more individuals control anger, the less they externalize it. At the neural level, individual differences in anger expression and control can be predicted by the same GM–WM network. As expected, this network included lateral and medial frontal regions, the insula, temporal regions, and the precuneus. The higher the concentration of GM–WM in this brain network, the higher the level of externalization of anger, and the lower the anger control. These results expand previous findings regarding the neural bases of anger by showing that individual differences in anger control and expression can be predicted by morphometric features.

A note on potential gains in precision of radiation risk estimates from joint analysis.

In estimating radiation-related risk of cancer and other diseases based on the RERF Life Span Study (LSS), joint analyses can be performed where multiple health outcome endpoints are combined in the same model, allowing some parameters to be estimated in common among all endpoints with possible increase in precision of radiation risk and other model parameter estimates. Using as a basis excess relative risk (ERR) and excess absolute risk (EAR) models of the type commonly used in analysis of LSS data at RERF, we use maximum likelihood theory to compute the asymptotic relative standard error of endpoint-specific radiation effect and other parameter estimates using joint analyses as compared to traditional independent analysis. We show that some gains in precision of endpoint-specific radiation risk parameter estimates can be achieved by sharing effect modifier and other model parameters, but only small or negligible gains in precision are achieved for endpoint-specific background modifying or effect modifying parameters when other model parameters are shared. The magnitude of the precision gain for radiation risk estimates depends on the number of endpoints, the baseline incidence rate of the endpoint, and the type of model being used.

A re-sampling statistics based imprecise moment independent global sensitivity analysis methodology with limited data of uncorrelated and correlated geotechnical properties

Traditional Global Sensitivity Analysis (GSA) is often performed using variance-based Sobol’s GSA, considering statistics of inputs estimated from limited data to be precise. This can lead to inaccuracies in the importance ranking of inputs due to negligence of – i) epistemic uncertainties in their statistics, and ii) role of other moments of outputs in Sensitivity Indices (SIs). This study overcomes these limitations by proposing an imprecise moment independent GSA methodology. The methodology couples re-sampling statistical techniques, i.e., Jackknifing and Bootstrapping with Monte-Carlo Simulations based Borgonovo’s moment independent GSA to estimate distributions of SIs. The re-sampling techniques estimate epistemic uncertainties in the statistics of inputs by generating re-constituted samples. These epistemic uncertainties are then propagated via Borgonovo’s GSA to estimate their imprecise moment independent SIs. The methodology is further coupled with Nataf’s transformation and Moving Least Square-Response Surface Methods to deal with problems of correlated inputs and lack of explicit input-output relationships. The generalized nature of methodology is demonstrated for three real case studies (one tunnel and two slopes), with different correlation structures between inputs, input-outputs relationships (explicit/implicit), and failure mechanisms. The methodology estimated the distributions of SIs of inputs instead of their point estimates, indicating the propagation of epistemic uncertainties truthfully. The Jackknife-GSA was significantly efficient (∼99 %) with negligible differences in statistics of SIs as compared to Bootstrap-GSA. The correlation between inputs significantly affects the statistics of SIs. Further, a reduction in the imprecision of SIs of inputs with their increasing sample sizes was observed, which is consistent with the general understanding.

ChatGPT: a force for good? Using the I.D.E.As framework to ’develop and empower’ students

Generative artificial intelligence (GenAI) tools (for example, ChatGPT) are a specific application of artificial intelligence which generate novel content in response to questions or instructions. Their use in education has generated diverse responses. Critics express concerns that they might discourage students from engaging in independent analysis, evaluation and problem-solving, and hinder the development of critical thinking skills (Farrokhnia et al., 2023). Another concern is the potential effect on the development of meaningful student-teacher and peer-to-peer interactions, important in fostering student belonging, and developing communication and social-emotional skills. Conversely, supporters see these tools as valuable resources providing instant access to tailored information, facilitating personalised learning experiences and promoting student engagement. They argue that rather than undermining higher-order skills like critical thinking and evaluative judgment, GenAI tools can enhance their development. Some propose they may even help address the ‘awarding gap’ (Fido and Wallace, 2023), with students using such tools as a ‘dialogic tutor’, to personalise learning and so develop their knowledge and understanding (Compton, 2023a). The presentation introduced the ‘I.D.E.As framework’ (Hack, 2023), a resource developed to help structure thinking and curate ideas regarding using GenAI tools in learning and teaching. The session explored ways in which GenAI tools may be used as a ‘force for good’, to enhance the student learning experience and support their individual learning development. Resources to ‘develop and empower’ students as independent learners were shared, providing an opportunity for attendees to reflect on how/whether they might be incorporated into their own learning and teaching contexts.

Growth of Renal Cancer Cell Lines Is Strongly Inhibited by Synergistic Activity of Low-Dosed Amygdalin and Sulforaphane

Background: Plant derived isolated compounds or extracts enjoy great popularity among cancer patients, although knowledge about their mode of action is unclear. The present study investigated whether the combination of two herbal drugs, the cyanogenic diglucoside amygdalin and the isothiocyanate sulforaphane (SFN), influences growth and proliferation of renal cell carcinoma (RCC) cell lines. Methods: A498, Caki-1, and KTCTL-26 cells were exposed to low-dosed amygdalin (1 or 5 mg/mL), or SFN (5 µM) or to combined SFN-amygdalin. Tumor growth and proliferation were analyzed by MTT, BrdU incorporation, and clone formation assays. Cell cycle phases and cell cycle-regulating proteins were analyzed by flow cytometry and Western blotting, respectively. The effectiveness of the amygdalin–SFN combination was determined using the Bliss independence model. Results: 1 mg/mL amygdalin or 5 µM SFN, given separately, did not suppress RCC cell growth, and 5 mg/mL amygdalin only slightly diminished A498 (but not Caki-1 and KTCTL-26) cell growth. However, already 1 mg/mL amygdalin potently inhibited growth of all tumor cell lines when combined with SFN. Accordingly, 1 mg/mL amygdalin suppressed BrdU incorporation only when given together with SFN. Clonogenic growth was also drastically reduced by the drug combination, whereas only minor effects were seen under single drug treatment. Superior efficacy of co-treatment, compared to monodrug exposure, was also seen for cell cycling, with an enhanced G0/G1 and diminished G2/M phase in A498 cells. Cell cycle regulating proteins were altered differently, depending on the applied drug schedule (single versus dual application) and the RCC cell line, excepting phosphorylated Akt which was considerably diminished in all three cell lines with maximum effects induced by the drug combination. The Bliss independence analysis verified synergistic interactions between amygdalin and SFN. Conclusions: These results point to synergistic effects of amygdalin and SFN on RCC cell growth and clone formation and Akt might be a relevant target protein. The combined use of low-dosed amygdalin and SFN could, therefore, be beneficial as a complementary option to treat RCC. To evaluate clinical feasibility, the in vitro protocol must be applied to an in vivo model.

Characterization of Cytoskeletal Profilin Genes in Plasticity Elongation of Mesocotyl and Coleoptile of Maize Under Diverse Abiotic Stresses

The plasticity elongation of mesocotyl (MES) and coleoptile (COL) largely determines the morphology of maize seedlings under abiotic stresses. The profilin (PRF) proteins play a pivotal role in cytoskeleton dynamics and plant development via regulating actin polymerization. However, little is known about whether and how the expression of the ZmPRF gene family regulates MES and COL elongation in maize under adverse abiotic stresses. Here, a total of eight ZmPRF gene members were identified in the maize genome. They were mainly located in the cytoplasm, chloroplast, and mitochondrion, and clearly divided into four classes, based on phylogenetic analysis. Segmental duplication was the main driver for the expansion of ZmPRF genes. Ka/Ks analysis indicated that most ZmPRF genes were intensely purified and selected. Promoter cis-element analysis suggested their potential roles in response to growth and development, stress adaption, hormone response, and light response. The protein–protein interaction network and two independent RNA-sequencing analyses revealed that eight ZmPRF genes and their thirty-seven interacting genes showed varied expression patterns in MES and COL of three maize genotypes under different sowing depths, 24-epibrassinolide application, and light spectral-quality treatments, of which ZmPRF3.3 was a potential core conserved gene for breeding application. Moreover, the quantitative real-time PCR (qRT-PCR) verified that the relative expression levels of most ZmPRF genes in MES and COL under above treatments were significantly correlated with the plasticity elongation of MES and COL in maize. Therefore, these results perform a comprehensive overview of the ZmPRF family and will provide valuable information for the validation of the function of ZmPRF genes in maize development under diverse abiotic stress.

The Impact of Leadership Styles, Cultural Dimensions and Values on Academic Leaders

This study investigates the cultural dimensions, values, and leadership styles of school leaders in Indian K-12 and European schools, specifically focusing on cross-cultural differences. The objective is to explore how leadership styles and cultural orientations differ between Indian and European school leaders and to examine how these variations impact organizational culture and decision-making processes. A non-experimental quantitative research design was employed, utilizing standardized instruments such as the Multi-factor Leadership Questionnaire (MLQ-Form 6S), OCTAPACE, and the Scale of Individual Cultural Values (CV) to measure leadership styles and cultural dimensions. Data were collected from 165 Indian leaders across Punjab, Haryana, Delhi-NCR, and Uttar Pradesh, as well as 156 European leaders from Croatia, Hungary, Poland, and others, using purposive and convenience sampling methods. Independent t-tests and Discriminant Function Analysis (DFA) were used to compare the two regions' leadership styles and cultural orientations. The findings reveal that Indian school leaders predominantly demonstrate transformational and laissez-faire leadership behaviors, influenced by hierarchical structures prioritizing collective goals and authority delegation. Indian leaders scored higher on openness, trust, and collaboration dimensions, which align with collectivist and hierarchical cultural norms. In contrast, European leaders emphasized confrontation, authenticity, and individual autonomy, reflecting a preference for more egalitarian and individualistic decision-making approaches. These insights contribute to a deeper understanding of how leadership and cultural values shape educational practices in different contexts.

Acceptance of Digital Discharge Management Interventions Among Patients After Bariatric Surgery: A Cross-Sectional Study

Bariatric surgery is an effective long-term treatment for severe obesity, but relapse rates remain high. Digital interventions can enhance patient care, yet research on the intention to use digital discharge management interventions is lacking. This study aims to assess the behavioral intention to use digital discharge management interventions after bariatric surgery and to identify differences in sociodemographic and medical characteristics, as well as potential key drivers and barriers. A cross-sectional study with N = 514 patients was conducted using the Unified Theory of Acceptance and Use of Technology (UTAUT). Mean scores for behavioral intention and predictors were calculated. Group differences were analyzed with independent t-tests and analyses of variance with post hoc tests. Drivers and barriers were assessed through multiple hierarchical regression analysis. The behavioral intention to use digital discharge management interventions was high. Significant predictors included age (β = −0.17, p &lt; 0.001), eHealth literacy (β = 0.10, p = 0.037), internet anxiety (β = −0.15, p = 0.003), and time since bariatric operation (β = −0.13, p = 0.005). The predictors performance expectancy (β = 0.23, p &lt; 0.001), effort expectancy (β = 0.36, p &lt; 0.001), and social influence (β = 0.26, p &lt; 0.001) were significantly positive key factors. These results confirm the need for implementing digital discharge interventions after bariatric surgery, with various drivers and barriers identified for application usage.

Multiomics Profiling of Plasma Reveals Molecular Alterations Prior to a Diagnosis with Stroke Among Chinese Hypertension Patients.

We aimed to investigate the correlation between plasma proteins and metabolites and the occurrence of future strokes using mass spectrometry and bioinformatics as well as to identify other biomarkers that could predict stroke risk in hypertensive patients. In a nested case-control study, baseline plasma samples were collected from 50 hypertensive subjects who developed stroke and 50 gender-, age- and body mass index-matched controls. Plasma untargeted metabolomics and data independent acquisition-based proteomics analysis were performed in hypertensive patients, and 19 metabolites and 111 proteins were found to be differentially expressed. Integrative analyses revealed that molecular changes in plasma indicated dysregulation of protein digestion and absorption, salivary secretion, and regulation of actin cytoskeleton, along with significant metabolic suppression. C4BPA, Caprolactam, Col15A1, and HBB were identified as predictors of stroke occurrence, and the Support Vector Machines (SVM) model was determined to be the optimal predictive model by integrating six machine-learning classification models. The SVM model showed strong performance in both the internal validation set (area under the curve [AUC]: 0.977, 95% confidence interval [CI]: 0.941-1.000) and the external independent validation set (AUC: 0.973, 95% CI: 0.921-0.999).

The Relationship Between Nursing Students' Perceptions of Individualized Care and Quality of Care: A Descriptive Cross-sectional Study

This study aims to evaluate nursing students' perceptions of individualized care and quality of care and examine the relationship between them. The population of this descriptive, cross-sectional study consisted of 2nd, 3rd, and 4th-year students (n=416) who were enrolled in the nursing department of a state university. The sample consisted of 275 nursing students. Data were collected online between April and May 2022 using the "Student Information Form", “Individualized Care Scale- Nurse (ICS-A Nurse)” and “Caring Behaviors Inventory-24 (CBI-24)”. Data was analyzed using the Independent samples t-tests, ANOVA, correlation analysis, and regression analysis. The CBI-24 and ICS-A total mean scores were 5.01±0.58 and 4.12±0.53, respectively. The most commonly perceived barrier to providing individualized care was “communication problems”. The ICS-A was found to explain approximately 42.6% of the variance in students' CBI-24 mean score (p

Monitoring the level of health and inequality over time using the Population Health Performance Index

Abstract Background The Population Health Performance Index (PHPI) is a composite measure that combines average health outcomes and their distribution within a population, thereby allowing for a comprehensive assessment of population health and inequality. The aim of this study is to examine the extent to which the PHPI adds value to monitoring of health and inequality over time in comparison to absolute and relative inequality. Methods Data from the WHO Equity Monitor database on under-5 mortality disaggregated by wealth quintiles for Nepal, Ethiopia, Peru, Rwanda, and Zimbabwe were analysed using different years between 1994 and 2020. Descriptive comparative statistics were employed to assess trends in the PHPI and conventional pairwise measures of absolute and relative inequalities, with consideration of different weights for inequality aversion. Results PHPI trends correspond to the trend of absolute inequality in all five countries, although they exhibit less volatility over time. Relative inequality follows the trend of the PHPI in only two of the five countries (Ethiopia and Zimbabwe), reflecting the methodology utilised for the PHPI. PHPI scores vary based on different inequality aversion weights, highlighting the sensitivity of the index to methodological adjustments. Conclusions The PHPI demonstrates notable convergence of trends with conventional methods, thereby substantiating its utility as a comprehensive tool for the simultaneous measurement of health and inequality. As it is able to consider both absolute and relative inequality within its own calculations, the PHPI allows for independent analysis of its two key components. The PHPI facilitates a more nuanced understanding of population health and may be more easily understood by policymakers and other stakeholders. As it does not address the underlying causes of changes in health inequalities, further research is necessary to supplement monitoring activities. Key messages • PHPI is a valuable tool for health and inequality monitoring by integrating both average health and disparity in a single index, facilitating communication of trends over time. • The dual focus complements conventional methods with its multidimensionality and may enable stakeholders to assess health and inequalities in alignment with broader health policy objectives.

Statistical analysis of seroprevalence and risk factors of hepatitis C in Nowshera District, Pakistan

Hepatitis C virus (HCV) transmission remains a significant public health concern. It is well documented globally, however, Nowshera district, Pakistan, is lacking such profile. This study aims to explore the relationship between HCV infection and several risk factors, including socio-demographic, clinical and personal life-style factors. This study using a cross-sectional design, examined 606 randomly selected individuals visiting the Pathology department at Qazi Hussain Medical Complex and District Headquarter (DHQ) Hospital Nowshera between May 1, 2022 and Jun 30, 2023. This research investigation employed a methodical approach involving formal interviews in conjunction with structured questionnaires to gather comprehensive information related to socio-demographic characteristics, clinical history, and personal hygiene practices. We collected 10 ml of blood samples and tested the separated serum to identify markers using the Immuno-Chromatographic Test (ICT) and the Enzyme-Linked Immunosorbent Assay (ELISA). IBM SPSS Statistics version 27 was used for data analysis. To measure the association between dependent variable and independent variables, a chi-square and risk analysis was carried out; multiple logistic regression was employed for modeling the risk factors associated with independent variable. A statistical significance level was established at a P-value of less than 0.05. In present study, which included 606 participants, 12% were found to be infected with HCV. Importantly it revealed that facial shave at barber (adjusted odds ratio, aOR = 40.65, p = 0.000) represents the most prevalent mode of HCV transmission. Additionally, a strong association was observed between HCV infection and history of past surgery (RR = 2.98, p = 0.001), HCV infected family member (aOR = 4.28, p = 0.001), workplace injuries (aOR = 6.68, p = 0.000), history of hospital admission (RR = 2.09), practicing ear and nose piercing (aOR = 5.01, p = 0.001), dental treatment (RR = 2.31) and the frequency of injections (aOR = 8.607, p = 0.000). These findings underscore the pressing need for targeted interventions. This study highlights the paramount importance of understanding the modes of HCV transmission and their associated risk factors. The results emphasize the need for educational initiatives, both within the healthcare sector and among the general public, to combat HCV transmission effectively. By implementing sterilization procedures and increasing awareness, we can make significant strides in reducing the burden of HCV infection. Moreover, proactive measures within families can help contain the spread of the virus, ultimately contributing to improved public health.

Microsatellites explorer: A database of short tandem repeats across genomes

Short tandem repeats (STRs) are widespread, repetitive elements, with a number of biological functions and are among the most rapidly mutating regions in the genome. Their distribution varies significantly between taxonomic groups in the tree of life and are highly polymorphic within the human population. Advances in sequencing technologies coupled with decreasing costs have enabled the generation of an ever-growing number of complete genomes. Additionally, the arrival of accurate long reads has facilitated the generation of Telomere-to-Telomere (T2T) assemblies of complete genomes. Nevertheless, there is no comprehensive database that encompasses the STRs found per genome across different organisms and for different human genomes across diverse ancestries. Here we introduce Microsatellites Explorer, a database of STRs found in the genomes of 117,253 organisms across all major taxonomic groups, 15 T2T genome assemblies of different organisms, and 94 human haplotypes from the human pangenome. The database currently hosts 406,758,798 STR sequences, serving as a centralized user-friendly repository to perform searches, interactive visualizations, and download existing STR data for independent analysis. Microsatellites Explorer is implemented as a web-portal for browsing, analyzing and downloading STR data. Microsatellites Explorer is publicly available at https://www.microsatellitesexplorer.com.

A comparison between in vitro and randomized in situ models for remineralization of artificial enamel lesions

The randomized study aimed to evaluate the comparability of in situ (iS) and in vitro (iV) study protocols regarding remineralization of artificial enamel lesions. Two toothpastes (group A: 1450 ppm sodium fluoride, group B: placebo 0 ppm F-), were investigated. IV, a pH-cycling model with toothpaste slurry treatment was applied for 10d. IS, remineralization was performed in 9 participants wearing splints with embedded enamel samples for 10 and 21d, randomly allocated to groups A and B. Samples were scanned by X-ray micro-computed tomography (μCT) and grayscale value line profiles corresponding to mineral density (rel.ΔZ) were analyzed. Statistical analyses were performed using MedCalc Statistical Software, v22.021. T-Test for dependent and independent data and analysis of variance (ANOVA) were used for further analyses (α = 0.05). Rel.ΔZ of fluoride treated samples (A) were iV = 40.2%, iS 10d = 11.5% and iS 21d = 46.1% (p > 0.05). Rel.ΔZ of placebo treated samples (B) were: iV = − 6.2%, iS 10d = 25.2% and iS 21d = 11.0% (p > 0.05). Remineralization potential of both toothpastes was significantly different regarding iV (p < 0.001) and iS after 21d (p = 0.034), while in case of iS 10d no significant difference was detected (p = 0.4). Despite different study protocols the μCT results after remineralization were comparable between iV and iS. The results suggest that selected studies can be carried out in faster, simplified iV studies using pH-cycling instead of iS studies.

Identification of JAZF1, KNOP1, and PLEKHA1 as causally associated genes and drug targets for Alzheimer's disease: a summary data-based Mendelian randomization study.

There is a growing body of evidence indicating the significant role of the immune system and immune cells in the progression of Alzheimer's disease (AD). However, the exact role of genes from various immune cell types in AD remains unclear. We aimed to utilize summary data-based Mendelian randomization (SMR) to explore the potential causal relationships between genes in specific immune cells and the risk of AD. By utilizing data sets of expression quantitative trait loci (eQTL) for 14 different immune cell types and large-scale AD genome-wide association study (GWAS), we employed SMR to identify key genes associated with AD within specific immune cells. Sensitivity analyses, including F-statistic, colocalization, and assessment of horizontal pleiotropy, were further conducted to validate the discovered genes. In addition, replication analyses were performed in AD GWAS from the FinnGen consortium. Finally, we further identified existing drugs that target or interact with the druggable genes and reviewed the studies about the associations between these drugs and AD. SMR analysis revealed 342 genes associated with AD across 14 immune cell types. Further sensitivity analyses identified nine genes, CTSH, FCER1G, FNBP4, HLA-E, JAZF1, KNOP1, PLEKHA1, RP11-960L18.1, and ZNF638 that had significant associations with AD across nine specific immune cell types. JAZF1, KNOP1 and PLEKHA1 were replicated in an independent analysis usingthe GWAS data. The review on gene-related drugs also supported these findings. Our research suggests that the expression of the genes JAZF1, KNOP1, and PLEKHA1 in specific immune cell types is related to the risk of AD.

Significantly enhancing enzymatic saccharification of poplar waste through the effective removal of lignin and hemicellulose by 1-butanol-Na3PO4-water treatment

Na3PO4-H2O-assisted 1-butanol pretreatment was established for changing the structure of poplar waste and improving its enzymatic efficiency. After poplar waste was pretreated at 180 °C for 60 min, 51.3 % of xylan and 52.5 % of lignin were eliminated. After the enzymatic saccharification of treated poplar waste, the yield of reducing sugars reached 75.3 %. A series of characterizations demonstrated that Na3PO4-H2O-assisted 1-butanol pretreatment altered the poplar composition with improved cellulose accessibility from 231.2 to 459.5 mg/g and reduced surface area of lignin from 384.2 to 255.3 m2/g. Molecular simulations, quantum chemical calculations and independent gradient model analysis were performed on the poplar model, and the 1-butanol-Na3PO4-H2O pretreatment resulted in the increase of electrostatic and van der Waals repulsion forces in lignin and xylan, explaining the efficient lignin and xylan removal. A comprehensive interpretation of mass balance and energy flow was applied to assess the entire pretreatment process. This eco-friendly 1-butanol-Na3PO4-H2O pretreatment effectively broken the encapsulation of cellulose by enzyme inhibitors and improve biomass pretreatment effect.

Identification of SLC31A1 as a prognostic biomarker and a target for therapeutics in breast cancer

Copper-induced cell death is regulated through protein lipoylation, which is critical for gene expression and phenotypic regulation. Neverless, the role of Cuproptosis-related genes in breast cancer (BC) remains unknown. This study aimed to construct a prognostic signature based on the expression of Cuproptosis-related genes in order to guide the diagnosis and treatment for BC. Cuproptosis-related genes prognostic signature has ata of 1250 BC tissues and 583 normal breast tissues were obtained from The Cancer Genome Atlas (TCGA), Genotype Tissue Expression (GTEx), and GEO GSE65212. The prognostic signature was established and evaluated with nineteen Cuproptosis-related genes. A series of in silico analyses based on SLC31A1, included expression analysis, independent prognostic analysis, correlation analysis, immune-related analysis and survival analysis. Finally, a series of cell experiments (including quantitative real-time polymerase chain reaction and western blot), and mice experiments were applied to evaluate the impact of SLC31A1 on BC. Cuproptosis-related genes prognostic signature has good predictive promising for survival in BC patients. We discovered that SLC31A1SLC31A1 was overexpressed in BC and was its independent prognostic factor. High expression of the SLC31A1 was correlated with poor prognosis and immune infiltrating of BC. SLC31A1 expression is associated with immune, chemotherapeutic and targeted therapy outcomes in BC. The proliferation, migration, and invasiveness of Her2 + enriched BC cells were decreased by SLC31A1 knockdown, also resulting in a decrease in tumor volume in mouse model. SLC31A1 is a candidate biomarker or therapeutic target in precision oncology, with diagnostic and prognostic significance in BC.