Generalized Linear Models Research Articles

Study of the Genetic Mechanisms of Siberian Stone Pine (Pinus sibirica Du Tour) Adaptation to the Climatic and Pest Outbreak Stresses Using Dendrogenomic Approach

A joint analysis of dendrochronological and genomic data was performed to identify genetic mechanisms of adaptation and assess the adaptive genetic potential of Siberian stone pine (Pinus sibirica Du Tour) populations. The data obtained are necessary for predicting the effect of climate change and mitigating its negative consequences. Presented are the results of an association analysis of the variation of 84,853 genetic markers (single nucleotide polymorphisms—SNPs) obtained by double digest restriction-site associated DNA sequencing (ddRADseq) and 110 individual phenotypic traits, including dendrophenotypes based on the dynamics of tree-ring widths (TRWs) of 234 individual trees in six natural populations of Siberian stone pine, which have a history of extreme climatic stresses (e.g., droughts) and outbreaks of defoliators (e.g., pine sawfly [Neodiprion sertifer Geoff.]). The genetic structure of studied populations was relatively weak; samples are poorly differentiated and belong to genetically similar populations. Genotype–dendrophenotype associations were analyzed using three different approaches and corresponding models: General Linear Model (GLM), Bayesian Sparse Linear Mixed Model (BSLMM), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), respectively. Thirty SNPs were detected by at least two different approaches, and two SNPs by all three. In addition, three SNPs associated with mean values of recovery dendrophenotype (Rc) averaged across multiple years of climatic stresses were also found by all three methods. The sequences containing these SNPs were annotated using genome annotation of a very closely related species, whitebark pine (P. albicaulis Engelm.). We found that most of the SNPs with supposedly adaptive variation were located in intergenic regions. Three dendrophenotype-associated SNPs were located within the 10 Kbp regions and one in the intron of the genes encoding proteins that play a crucial role in ensuring the integrity of the plant’s genetic information, particularly under environmental stress conditions that can induce DNA damage. In addition, we found a correlation of individual heterozygosity with some dendrophenotypes. Heterosis was observed in most of these statistically significant cases; signs of homeostasis were also detected. Although most of the identified SNPs were not assigned to a particular gene, their high polymorphism and association with adaptive traits likely indicate high adaptive potential that can facilitate adaptation of Siberian stone pine populations to the climatic stresses and climate change.

Brain structural associations of syntactic complexity and diversity across schizophrenia spectrum and major depressive disorders, and healthy controls

Deviations in syntax production have been well documented in schizophrenia spectrum disorders (SSD). Recently, we have shown evidence for transdiagnostic subtypes of syntactic complexity and diversity. However, there is a lack of studies exploring brain structural correlates of syntax across diagnoses. We assessed syntactic complexity and diversity of oral language production using four Thematic Apperception Test pictures in a sample of N = 87 subjects (n = 24 major depressive disorder (MDD), n = 30 SSD patients both diagnosed according to DSM-IV-TR, and n = 33 healthy controls (HC)). General linear models were used to investigate the association of syntax with gray matter volume (GMV), fractional anisotropy (FA), axial (AD), radial (RD), and mean diffusivity (MD). Age, sex, total intracranial volume, group, interaction of group and syntax were covariates of no interest. Syntactic diversity was positively correlated with the GMV of the right medial pre- and postcentral gyri and with the FA of the left superior-longitudinal fasciculus (temporal part). Conversely, the AD of the left cingulum bundle and the forceps minor were negatively correlated with syntactic diversity. The AD of the right inferior-longitudinal fasciculus was positively correlated with syntactic complexity. Negative associations were observed between syntactic complexity and the FA of the left cingulum bundle, the right superior-longitudinal fasciculus, and the AD of the forceps minor and the left uncinate fasciculus. Our study showed brain structural correlates of syntactic complexity and diversity across diagnoses and HC. This contributes to a comprehensive understanding of the interplay between linguistic and neural substrates in syntax production in psychiatric disorders and HC.

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

This study investigates the potential of leveraging molecular properties, as determined by MD-LOVIs software and machine learning techniques, to predict the ability of compounds to cross the blood-brain barrier (BBB). Accurate prediction of BBB permeation is critical for the development of central nervous system (CNS) drugs. The study applies various machine learning models, including both classification and regression techniques, to predict BBB passage and molecular activity. Notably, classification models such as GBM-AWV (accuracy = 0.801), GLM-CN (accuracy = 0.808), SVMPoly-means (accuracy = 0.980), SVMPoly-AC (accuracy = 0.980), SVMPoly-MI_TI_SI (accuracy = 0.900), SVMPoly-GI (accuracy = 0.900), RF-means (accuracy = 0.870), and GLM-means (accuracy = 0.818) demonstrate high accuracy in predicting BBB passage. In contrast, regression models like ES-RLM-AG (R2 = 0.902), IB-IBK (R2 = 0.82), IB-Kstar (R2 = 0.834), IB-MLP (R2 = 0.843), and DRF-AWV (R2 = 0.810) exhibit strong performance in predicting molecular activity. The results show that classification models like GBM-AWV, GLM-CN, and SVMPoly variants, as well as regression models like ES-RLM-AG and IB-MLP, achieve high performance, demonstrating the effectiveness of machine learning in predicting BBB permeability. The computational methods employed in this study include the MD-LOVIs software for generating molecular descriptors and several machine learning algorithms, including gradient boosting machines (GBM), generalized linear models (GLM), support vector machines (SVM) with polynomial kernels, random forests (RF), ensemble regression models, and instance-based learning algorithms. These models were trained and validated using various datasets to predict BBB passage and molecular activity, with the performance metrics reported for each model. Standard computational techniques were employed throughout, ensuring the reliability of the predictions.

Impacts of shifting from single-species pine forests to distinct agroforestry models on soil fertility, exchangeable cations, and microbial functions

The transition from monoculture to mixed-species agroforestry systems affects soil organic matter and microbial activity. However, the specific dynamics of these changes, particularly within medicinal plant-based agroforestry, remain underexplored. This study investigates the impact of monoculture Pine (Pinus massoniana) forests and four agroforestry models: (M1) Pinus massoniana and Alpina oxyphylla, (M2) Pinus massoniana and Ficus simplicissima, (M3) Pinus massoniana and Amomum villosum, and (M4) Pinus massoniana and Curcuma longa on soil properties and microbial activity in rhizosphere and non-rhizosphere environments. Our results showed significantly higher pH (4.80) and total nitrogen (N) content (1.77 g kg−1) in the rhizosphere of model (M4) compared to (CK). Total organic carbon (TOC) and carbon fractions (POC, DOC, and MBC) also differed significantly across monoculture and agroforestry models, with highest TOC concentrations (31.70 g kg⁻1) in rhizosphere of CK. Exchangeable cations, including Ca2⁺, and Mg2⁺ were significantly higher in the rhizosphere of agroforestry models compared to CK, particularly in M4, where Ca2⁺ was recorded at 12.03 cmol kg−1 with the highest percent base saturation (PBS) at 90.17%. Enzymes leucine aminopeptidase and polyphenol oxidase varied significantly, with higher activity in the rhizosphere of agroforestry models and greater activity in non-rhizosphere of monoculture. Soil microbial respiration (MRes) revealed substantial differences, with an average 17% decrease in rhizosphere soil for models M2 and M4 and a 20.83% reduction in non-rhizosphere soil for model M1 compared to CK. Generalized Linear Model (GLM) demonstrated a significant positive correlation between TOC and MRes (R2 = 0.885, p < 0.01), indicating that higher TOC levels are linked with increased MRes. In conclusion, model M4 most effectively enhanced soil fertility and nutrient availability followed by the other agroforestry models tested. This suggests that integrating medicinal plants into agroforestry systems is a viable strategy for improving ecosystem functioning compared to monoculture practices.

Quality of life and survivorship in patients with low-grade ovarian cancer

ObjectiveHigh-grade (HGOC) and low-grade ovarian carcinoma (LGOC) are distinct malignancies with different biological features, treatment paradigms, and life expectancies. However, differences in quality of life (QOL), sleep, and depressive symptoms have not been examined by grade, and neither have inflammatory profiles associated with these symptoms. We aim to characterize QOL and biomarkers by OC grade. MethodsParticipants included patients with HGOC (N = 578) or LGOC (N = 85). Participants completed baseline assessments of psychosocial factors prior to primary surgery or neoadjuvant chemotherapy and contributed saliva for cortisol and blood for interleukin-6 (IL-6) quantification. Samples were collected intraoperatively to quantify tumor cortisol. General linear models were used to examine differences in biological and psychological variables by grade. ResultsAt baseline, patients with LGOC reported less depression (p = 0.018) and sleep disturbances (p = 0.014), but no significant difference in depressive mood (p = 0.11) or QOL (p = 0.51) compared to patients with HGOC, adjusting for age and disease stage. There were trends towards lower tumor cortisol levels (p = 0.078) in LGOC compared to HGOC. One-year post-diagnosis, we found a significant improvement in QOL and fatigue, and a decrease in vegetative depression and IL-6 levels irrespective of grade. ConclusionsWe present the first characterization of psychosocial experiences of patients with LGOC. Despite having a better disease prognosis, patients with LGOC were just as likely to have mood disturbances as those with HGOC. There was a trend towards differences in tumor cortisol by grade. Our findings highlight the need to address well-being in patients with both low- and high-grade ovarian malignancies.

Association of Helicobacter pylori infection and white blood cell count: a cross-sectional study.

Helicobacter pylori is a type of Gram-negative microaerobic bacteria that inhabits the gastric mucosal epithelium. It can cause various gastrointestinal diseases including gastritis, peptic ulcer and gastric cancer. White blood cells (WBC) are common immune cells, the increase in whose countoften indicates the presence of an infection. Currently, the relationship between H. pylori and WBC count remains full of controversy. This study aims to further elucidate the effects of H. pylori on WBC count in a population undergoing physical examination. A total of 864 participants who underwent physical examination and 14C urea breath test (UBT) were retrospectively enrolled in this study from January to June 2021. The overall population was divided into H. pylori-negative (Hp-) and H. pylori-positive (Hp+) groups based on the disintegration per minute (DPM) value detected by UBT. Spearman's correlation analysis was used to assess the correlation between DPM and WBC count. General linear regression models were applied to assess the potential factors contributing to the increase in WBC count. Generalised additive model (GAM) was performed to identify the non-linear relationship between DPM and WBC count. Additionally, a piecewise linear regression was used to examine the threshold effect of the DPM on WBC count. 403 subjects were diagnosed with H. pylori infection. The WBC and platelet (PLT) counts in the Hp+ group were significantly higher than those in the Hp- group. Additionally, the prevalence of H. pylori infection gradually increased with the WBC count quartiles (38.89% and 54.67% in quartile 1 and quartile 4, respectively). Spearman's correlation analysis showed that the DPM value significantly correlated with WBC count (r=0.089, p=0.009) and PLT count (r=0.082, p=0.017). The linear model revealed a positive independent association of H. pylori infection and DPM with WBC count (βHp+=0.398 (95% CI 0.170, 0.625), p<0.001; βDPM=0.002 (95% CI 0.000, 0.0030), p=0.018). The results of the GAM and the piecewise linear regression suggested that the cut-off points of the association between DPM and WBC count were 40 and 155 of DPM, that is, the effect of DPM on WBC count varied with the difference of DPM <40, 40-155, and >155 (βDPM=-0.005 (95% CI -0.017, 0.007), p=0.423; βDPM=0.006 (95% CI 0.002, 0.013), p=0.047; and βDPM=-0.007 (95% CI -0.012, -0.002), p=0.004, respectively). H. pylori infection was independently and positively correlated with WBC count; however, the effect of DPM on WBC count varied across different WBC count intervals, suggesting distinct immunological responses at different stages of infection.

Microstructural alterations of cerebellar peduncles in multiple sclerosis: a diffusion tensor imaging study.

Ataxia, tremors, dysarthria, and sometimes impaired cognition are the signs of cerebellum involvement in multiple sclerosis (MS). These symptoms affect up to 80% of patients and are usually hard to treat. To find the underlying involvement of the cerebellum in MS, we assessed the microstructural alterations with DTI in the cerebellar peduncles of the affected subjects. We included 58 relapsing-remitting MS patients and 27 healthy controls. Patients were divided into 18 patients of relapsing-remitting MS with cerebellar impairment (RRMSc) and 40 without cerebellar impairment (RRMSnc). Using Diffusion Tensor Imaging (DTI), we calculated fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) metrics in all subjects. We also checked if there were associations between DTI metrics and clinical cerebellar measures (i.e., tremor severity and the scale for the assessment and rating of ataxia). ANOVA and post-hoc results showed significant differences in DTI metrics between RRMSc and HC and between RRMSnc and HC subjects. Inferior peduncle RD remained the only metric with a significant difference across all pairwise comparisons. The general linear model assessing the effects of the three study groups on the association between DTI metrics and clinical cerebellar measures yielded no significant result. Our study showed that DTI can mainly reveal significant differences between different MS groups and HCs. Our results imply the role of cerebellar peduncles in the pathophysiology of MS and that this role does not necessarily reflect the severity of cerebellar signs of the patients.

Enhancing wind erosion risk assessment through remote sensing techniques.

Preventing wind erosion and dust storms has always been a major concern in arid and semi-arid areas because of their negative effects on the environment. This study aims to utilize remote sensing and machine learning techniques to model, monitor, and predict the risk of wind erosion in Northeast Iran. Through an examination of relevant studies, a comprehensive review was conducted, leading to the identification of eight remote sensing indicators that exhibited the highest correlation with field data. These indicators were subsequently employed to model the risk of wind erosion in the study area. Various methods including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Generalized Linear Models (GLM) were employed to carry out the modeling process. The final method utilized a weighted average of the model, and the SDM statistical package was used to combine different approaches to decrease uncertainty when modeling and monitoring wind erosion in the area. The modeling results indicated that in 2008, the RF model performed the best (AUC = 0.92, TSS = 0.82, and Kappa = 0.96), while in 2023, the GBM model showed superior performance (AUC = 0.95, TSS = 0.79, and Kappa = 0.95). Therefore, the utilization of an ensemble model emerged as an effective approach to reduce uncertainty during the modeling process. By employing the ensemble model, the outcomes obtained accurately depicted an elevated intensity of wind erosion in the northeastern regions of the study area by 2023. Furthermore, considering the climatic scenarios and projected land use changes, it is anticipated that wind erosion intensity will experience a 23% increase in the central and southern parts of the study area by 2038. By taking into account the reliable results of the ensemble model, which offers reduced uncertainty, it becomes feasible to implement effective planning, optimal management, and appropriate measures to mitigate the progression of wind erosion.

Digital Literacy and the Livelihood Resilience of Livestock Farmers: Empirical Evidence from the Old Revolutionary Base Areas in Northwest China

Enhancing the livelihood resilience of livestock farmers in the old revolutionary base areas helps them to cope with the increasingly complex external risk shocks of recent years and promotes the sustainable development of regional agriculture. This study is based on survey data from 1047 livestock farmers in the Ningxia and Gansu provinces of the northwest old revolutionary base area. It incorporates the characteristics of livestock farmers and the elements of psychological capital into the sustainable livelihood analysis framework to construct a livelihood resilience index system. After measuring livelihood resilience, this paper uses a general linear regression model and a probit model to explore the impact and mechanism of digital literacy on the livelihood resilience of livestock farmers. The results show the following: (1) digital literacy has a significant positive effect on the livelihood resilience of livestock farmers, and the impact of different dimensions of digital literacy on different dimensions of livelihood resilience also varies. Additionally, this effect also shows the heterogeneity in different village clustering forms and different income groups. In areas inhabited by ethnic minorities and among moderate-income groups, the role of digital literacy on the livelihood resilience of livestock farmers is more significant. (2) The improvement of digital literacy has a significant positive impact on livelihood resilience through three different pathways: the “differential mode of association”, learning channels, and types of income. (3) Digital literacy has led to the psychological aspects of rural hollowing-out problems among livestock farmers, which is particularly evident in families with only one type of caregiving burden (either only left-behind elderly people or only left-behind children). This problem is more evident. Therefore, this paper poses that the advancement of agricultural and rural economic development in China should not only focus on the cultivation of farmers’ digital literacy but also accelerate the construction of digital infrastructure to ensure the long-term effective mechanism of improving digital literacy. At the same time, in the process of promoting digital rural areas, attention should be paid to the psychological isolation issues that the network era brings to farmers.

Midwives' readiness for midwife-led care: a mixed-methods study.

To integrate midwife-led care in Belgian maternity services, understanding whether midwives are primed of executing the change is needed. To explore Belgian midwives' readiness for midwife-led care and understand the underlying processes. A mixed-methods sequential study: 1) A survey including 414 practising midwives and 2) individual interviews with 12 (student) midwives. General linear model analysis was used to examine the trend between knowledge, self-efficacy and performance mean scores - indicators of midwife-led care readiness - proposed in a 27-item questionnaire. The Readiness Assessment Framework served as a template for qualitative thematic analysis. Template analysis illustrated the underlying mechanisms of midwifery-led care readiness: Governmental and institutional steering and rule-making functions, regulation and reimbursement, awareness of midwife-led care among stakeholders, capacity to extend primary care postpartum services to antenatal and intrapartum care and healthcare professionals' lack of awareness of available data of women's experiences and midwife-led care efficacy in Belgium. These qualitative findings contribute to the understanding of the significant trend with decreasing function for knowledge, self-efficacy and performance mean scores of 25 midwife-led care readiness indicators, and the two non-significant indicators referring to a physiological postpartum period. In determining midwife readiness for midwife-led care, we observed adequate knowledge mean scores, associated with low self-efficacy and even lower midwife-led care performance mean scores. Our findings suggest limited readiness for MLC in antenatal and intrapartum care. Belgian midwives are the domain experts of postpartum services but face challenges in extending midwife-led care to antenatal and intrapartum services.

Association Between Maternal Weight Perception Before and During Pregnancy and Postpartum Depression Status in Southern China

Objective: Postpartum depression (PPD) is a common complication after childbirth. Weight misperception can lead to self-esteem issues and mental health problems, especially in women and adolescents. The aim of this study was to investigate the association between weight perception before and during pregnancy and the status of PPD in Southern China. Methods: From October 2021 to November 2023, a multi-stage sampling method was used to recruit 2169 eligible mothers aged 18–49 who had delivered live-born singleton infants within 3 to 180 days postpartum. Anthropometric measurements and face-to-face questionnaire surveys were conducted to collect data. The Kappa test was used to assess the agreement between actual and perceived weight. The generalized linear model incorporating multiplicative interaction analysis was applied to explore the associations among variables. Results: The prevalence of PPD status was 18.0%. Among women, 35.2% perceived their pre-pregnancy weight (PPW) as abnormal, while 33.1% perceived their gestational weight gain (GWG) as inappropriate. There was poor agreement between maternal actual and perceived PPW/GWG (Kappa = 0.366, p &lt; 0.001; Kappa = 0.188, p &lt; 0.001), with 27.8% of women misperceiving their PPW and 52.1% misperceiving their GWG. The results of the general linear model indicated that women who perceived their PPW as underweight (β = 0.70, p = 0.016) or overweight/obese (β = 0.86, p &lt; 0.001), as well as those who perceived their GWG as excessive (β = 0.47, p = 0.028) were more likely to exhibit PPD status. The interaction analysis results showed that those who perceived their PPW as underweight and their GWG as insufficient (β = 1.75, p = 0.020), as well as those who perceived their PPW as overweight/obese and their GWG as excessive (β = 0.90, p = 0.001) had a positive interactive effect on the occurrence of PPD status, while underestimating PPW and GWG may be a protective factor against PPD status (β = −1.03, p = 0.037). Conclusion: These findings support that maternal weight perception plays a role in the development of PPD status. Further improvement is needed in personalized health education for weight management, both prior to and throughout the pregnancy period. This can help women reduce weight anxiety, better understand their body image, and potentially lower the risk of developing PPD.

A study of oral health and quality of life in patients with dyslipidemia

Objectives: This study analyzed factors influencing oral health and quality of life in patients with dyslipidemia using raw data from the first year (2019) and third year (2021), where the quality of life was assessed using the HINT-8 tool from the 8th National Health and Nutrition Survey data. Methods: Frequency analysis, descriptive statistics, general linear model analysis, and linear regression analysis were performed on the collected data using SPSS program 26.0, and the data of 1,926 participants were analyzed. Results: The mean quality of life (HINT-8) score was 0.78 out of 1.00. Quality of life according to general characteristics, showed significant differences according to gender, age, household income, education level, lifetime smoking status, and lifetime alcohol consumption. In terms of oral health, it significantly differed according to chewing discomfort, speaking discomfort, oral examination history, toothbrushing frequency, toothache history, untreated dental care needs, subjective oral health status and use of oral care products. The predictors of quality of life were gender, household income, education level, lifetime alcohol consumption, chewing discomfort, speaking discomfort, toothache history, and untreated dental care needs. Conclusions: These results confirm that oral health influences the quality of life of patients with dyslipidemia and are expected to provide basic data for research on improving oral health and quality of life in these patients.

Evaluation of beverage consumption patterns and oral health in Korean adolescents and young adults

Objectives: This study aimed to investigate the beverage consumption patterns and analyze the relationship between the frequency of beverage consumption and oral health in Korean adolescents and young adults who consumed large amounts of sugar from beverages. Methods: Data from the 8th Korea National Health and Nutrition Examination Survey (2019–2021) were analyzed, and 2,787 individuals aged 12-29 years were selected as the study subjects. A complex sample general linear model was used to assess the associations of subject general characteristics and oral health with the frequency of beverage consumption. Results: Both adolescents and young adults frequently consumed carbonated beverages 2.04 and 2.12 times per week, respectively. The most commonly consumed volume per drink was 200 cc. In both groups, the lower the average number of times teeth were brushed daily, the higher was the frequency of fizzy drink consumption. When teeth were brushed less than once daily, the frequency of carbonated drink consumption was 2.63 in adolescents and 2.22 in young adults. Conclusions: This study highlights the need for oral health education of adolescents and young adults on diet and beverage intake. Additionally, information should be provided on oral diseases associated with beverage consumption, such as dental caries and erosion.

Impact of Neuropsychological Assessment on Subjective Memory Complaints in Patients With Mild Cognitive Impairment.

Patients with Alzheimer's disease (AD), namely at an initial stage like amnestic cognitive impairment (aMCI), typically present with memory complaints. They also have difficulties regarding self-knowledge about their cognitive deficits. In clinical practice, a formal neuropsychological assessment is often done. The present study aimed to understand whether patients with aMCI retain the ability to monitor the success or failure in their performance during the neuropsychological assessment and adjust the report of memory complaints accordingly, as compared to healthy controls. Participants were patients with aMCI and healthy controls who were questioned about their own memory abilities using the Subjective Memory Complaints (SMC) scale, applied before and after the neuropsychological assessment protocol. A repeated measures General Linear Model was performed to analyze changes in SMC (within-subjects effects) after the neuropsychological assessment, in patients with aMCI and healthy controls (between-subjects effects). Eighty volunteers, 40 patients with aMCI and 40 healthy controls, participated in the study. Patients with aMCI showed lower MMSE scores, more depressive symptoms, and deficits in memory and learning, language and executive domains. Patients with aMCI had higher SMC scores [9.4(3.6)] than healthy controls [4.4(2.3)] before the neuropsychological assessment. A statistically significant interaction was found between the SMC and the diagnostic group, meaning that healthy controls decreased SMC [3.4(1.9)] after the neuropsychological assessment, whereas patients with aMCI kept high levels of SMC [9.6(3.9)]. In patients with aMCI, an inverse correlation between logical memory and the change in SMC was found, so that patients with lower scores in the logical memory test tended to increase their memory complaints after the assessment. Both patients with aMCI and healthy controls can monitor and update the impression about their memory abilities following a formal neuropsychological assessment. Patients with aMCI maintain a high SMC level, which is inversely associated with their memory performance. In practical terms, SMC should be measured consistently at a particular moment in time, preferably preceding the objective neuropsychological assessment.

The impact of LEP gene polymorphisms located at exon 2 (LEP-HinfI) and intron 2 (LEP-Sau3AI) on growth and reproductive traits in Saanen goats

Abstract. Leptin (LEP), alternatively recognized as the obesity gene, influences food consumption, energy balance, and lipid metabolism. Additionally, it plays a crucial role in energy metabolism, and variations in the LEP gene have been studied extensively among various livestock species. These investigations have unveiled correlations with traits such as meat quality, milk yield and composition, and growth characteristics. Nevertheless, the existing knowledge regarding its correlation with growth and reproductive traits in goats is comparatively limited, particularly when juxtaposed with studies of cattle. Hence, the objective of the current study was to investigate the relationship between polymorphisms in the intron 2 (Sau3AI) and exon 2 (HinfI) regions of the LEP gene and growth and reproductive traits in Saanen goats. The study comprised 185 Saanen goats in total. The PCR-RFLP (polymerase chain reaction restriction fragment length polymorphism) technique was employed to genotype these polymorphisms. Population genetic analysis included the number of effective alleles, heterozygosity, polymorphism information content, and compatibility with the Hardy–Weinberg equilibrium. The general linear model procedure's least-squares methods were used for the statistical analysis. The SNP (single-nucleotide polymorphism) located in exon 2 (LEP-HinfI) was monomorphic. Remarkably, the SNP located in intron 2 (LEP-Sau3AI) was associated with birth weight (p&lt;0.05) and average daily weight gain (p&lt;0.05). In this context, the AA genotype demonstrated higher birth weight and daily live weight gain means compared to other genotypes. No significant differences were observed in litter size, total weight gain, or morphometric measurements. The caprine LEP-Sau3AI polymorphism situated within intron 2 proved influential in traits critical for the profitability and sustainability of goat breeding. The findings of this study provide novel and valuable information for future research on the LEP gene in goats.

The THINC-it tool: temporal sensitivity to change over time

IntroductionCognitive dysfunction is believed to be among the core features of Bipolar Depression(BD-D). However, its evaluation and available treatments are limited. Here, we conducted a longitudinal follow-up clinical trial using the THINC-it tool to evaluate temporal sensitivity to change over time in cognitive function among patients with bipolar depression from a Chinese cohort. It is helpful to verify whether the scale can continuously and reliably measure cognitive function in different time points and reduce the measurement error caused by time factors. Hope our findings could provide insights into the significance of the THINC-it tool as an iterative clinical cognitive evaluation tool.MethodsA total of 120 patients with bipolar depression(40 males and 80 females, respectively) alongside 100 healthy controls(23 males and 77 females, respectively) were recruited in the study. All participants were interviewed for 8 weeks, using the 17-item Hamilton Depression Rating Scale (HAMD-17) and the Young Mania Rating Scale(YMRS). The primary dependent measure was the previously validated THINC-it tool, followed by psychometric analysis.ResultsRepeated measures of the THINC-it tool at baseline, one-week, and eight-week periods were conducted after controlling for age, gender, and education effects. Results from the general linear model revealed no significant time differences in variances(P > 0.05). Similarly, adjusting for confounding factors (age, gender, education, and HAMD-17 scores), results from the longitudinal analysis showed that there were no significant differences in cognitive impairment over time(P > 0.05). However, we found significant differences between BD-D and Healthy Control(HC) groups with regards to Spotter, Codebreaker, Trails, Perceived Deficits Questionnaire for Depression-5-items(PDQ-5-D), and THINC-it Total composite(P < 0.05), but not in Symbol Check (p = 0.191).ConclusionThese findings indicate that the THINC-it tool effectively detects sensitivity to change in groups and maintains stability at times, indicating that it is a feasible and reliable instrument for evaluating cognitive dysfunction in Chinese patients with bipolar depression.

Role of liraglutide on cardiotoxicity and gut microbiota changes induced by doxorubicin in rats

Abstract Background Cancer is one of the main causes of morbidity and mortality worldwide. Despite doxorubicin (DOX) being an effective chemotherapy drug, DOX-induced cardiotoxicity is the most severe side effect, limiting its use. There is no effective treatment for preventing DOX-induced cardiotoxicity. Gut microbiota seems to have a role in cardiovascular disease. Glucagon like peptide-1 (GLP-1) analogs such as liraglutide have shown benefits in cardiovascular diseases and seem to affect gut microbiota. Purpose To evaluate the role of liraglutide in modulating acute DOX-induced cardiotoxicity and gut microbiota. Methods Sixty male Wistar rats were allocated into four groups: Control (C), DOX (D), Liraglutide (L), and DOX + Liraglutide (DL). Animals in L and DL groups received a subcutaneous injection of 0.6 mg/kg liraglutide daily for 2 weeks. After 12 days, D and DL groups received an intraperitoneal injection of DOX (20 mg/kg). Rats were subjected to echocardiogram and isolated heart functional study 48 hours after DOX injection. At the end of the experiment, feces were collected to evaluate microbiota and short chain fatty acid concentration. Statistical analyses: Generalized linear model (GLM), ANCOVA and PERMANOVA (p&amp;lt;0.05). Results DOX-treated rats had worse cardiac function than rats that did not receive DOX in both echocardiography and isolated heart study; liraglutide did not change any functional parameters (Table). In feces, the phylum Bacteroidota was reduced in D and L groups compared to C and the phylum Pseudomonadota was increased in D and DL compared to C and L. Alpha-diversity did not differ between groups. Beta-diversity differed between C, D and L groups, and was similar between D and DL (Figure). Feces short chain fatty acid concentration was reduced in DOX-treated rats, with no effect from liraglutide (Figure). Conclusion DOX altered the function and composition of gut microbiota and caused acute cardiotoxicity; liraglutide changed gut microbiota and did not improve cardiac function.Table.Left ventricular functional dataFigure.Microbiota

A recent shift in the Puccinia striiformis f. sp. tritici population in Serbia coincides with changes in yield losses of commercial winter wheat varieties

Wheat yellow (stripe) rust, caused by the fungus Puccinia striiformis f.sp. tritici (Pst), is a devastating disease of wheat worldwide. The success of Pst is largely due to the pathogens ability to rapidly overcome host resistance, generating new races that are easily dispersed between territories through wind-borne transmission of Pst urediniospores. Thus, first signs of entry of new Pst races into a region is usually captured by changes in disease severity. To examine any alterations of winter wheat variety response to Pst infection in Serbia, we analyzed yield and Pst disease severity in field trials conducted in 2014, 2021, and 2023. We specifically focused on analyzing Pst disease severity at growth stages related to yield. Associations between qualitative variables (variety, year) and quantitative variables (yield in untreated plots, yield loss, and disease index (DI) of Pst infection) were analyzed using Principal Component Analysis with mixed data. A General Linear Model was used to investigate the most influential factors on yield, yield loss, and Pst infection. The results indicated that yellow rust disease severity increased over the past decade, suggesting a potential recent change in the Pst population in Serbia. Comparative population genetic analysis of Pst samples from the 2023 wheat season and those collected in Serbia in 2014 confirmed a potential change in the Pst population. In addition, we found that yield losses across wheat varieties varied independently of Pst infection levels, indicating that wheat varieties differ in their ability to overcome damage caused by high levels of Pst infection. Given that the level of pathogen pressure triggering susceptibility reactions is cultivar-specific, our study highlights the need for a deeper focus on the mechanisms underlying these differences. Expanding our understanding of the interactions between pathogens, plant defense responses, and the ability of cultivars to mitigate yield losses will better equip us to predict and prevent potential yield losses in commercial wheat varieties due to yellow rust in the future.

Cardiopulmonary exercise test findings are not the same in distinct types of cardiac amyloidosis

Abstract Background Cardiopulmonary exercise testing (CPET) is a widely used tool for risk stratification and prognosis in patients with chronic heart failure. In amyloid patients, cardiac amyloid deposits are associated with poor prognosis, increased risk for arrhythmias, sudden death, and subsequent exercise limitation due to restrictive cardiomyopathy. The type of amyloidosis and its affectation of the heart are different, and little is known about the CPET parameters according to the type of cardiac amyloidosis. Purpose In this study, we aim to compare CPET data in different types of amyloidosis to determine if CPET variables evaluated could have a significant difference that could impact the follow-up or prognosis of the patients based on the type of amyloidosis. Methods We conducted a retrospective cohort study of 194 patients with a diagnosis of cardiac amyloidosis referred for CPET from February 2010 to February 2024. Patients with at least one CPET data were included in our analysis. The type of amyloidosis, comorbidities, and echocardiogram data were retrieved from medical records. Patients were divided into 3 different groups according to the type of amyloidosis: AL Amyloidosis(AL), Variant TTR Amyloidosis (vTRR), and Wild Type TTR Amyloidosis (wtTTR). Individuals with CPET studies after heart transplant were excluded from the analysis. Fisher’s exact test or chi-square was used to compare categorical variables. ANOVA and Tukey-Kramer (TK) were used to compare continuous variables. Adjustment using General Linear Models (GLM) procedure was used to identify any confounder that could affect the results. A p-value &amp;lt;0.05 was considered significant. Results Out of the 194 patients, 66 had AL Amyloidosis, 40 had vTTR Amyloidosis, and 88 had wtTTR Amyloidosis. Most of the patients were male, and the male predominance was higher in the wtTRR group. There were 41 (62.1%) males in the AL group, 35 (87.5%) in the vTRR group, and 83 (94.3%) in the wtTTR group (p&amp;lt;0.0001). Comorbidities were balanced among the groups. Additional demographic and comorbidities data are shown in Table 1. From the evaluated CPET variables, VE/VCO2 nadir and slope were significantly higher in the wtTTR group when compared to the other groups, as well as Peak VE and Peak TV(Figure A). However, after adjusting for sex, BMI, and age, VE/VCO2 nadir was still significantly higher in the wtTRR group (p=0.003) compared to the other groups, as well as Peak VE, which was higher again in vTTR patients (p=0.001), and Peak TV (Table 2). Conclusion Ventilation variables evaluated in CPET showed alterations in the wtTTR group. These findings suggest that wtTRR amyloid patients could have a severe problem in ventilation that could be of significant relevance in future prognosis.

Modified ridge estimator in the Bell regression model

Abstract The Bell regression model (BRM), a member of the generalized linear models (GLMs), can be used when the dependent variable consists of overdispersed count data. The maximum likelihood estimator (MLE) is generally used to estimate unknown regression coefficients. The major drawback of the MLE is an inflated variance when multicollinearity problems occur. In this study, we proposed a new biased estimator to cope with the multicollinearity in the BRM. The simulation study is conducted to illustrate the performance of the proposed estimator over the MLE and Bell ridge estimator (BRE). Also, we give real data examples to approve the applicability of the proposed estimator in real data problems.