Identifying and overcoming COVID-19 vaccination impediments using Bayesian data mining techniques.

The COVID-19 pandemic has profoundly reshaped human life. The development of COVID-19 vaccines has offered a semblance of normalcy. However, obstacles to vaccination have led to substantial loss of life and economic burdens. In this study, we analyze data from a prominent health insurance provider in the United States to uncover the underlying reasons behind the inability, refusal, or hesitancy to receive vaccinations. Our research proposes a methodology for pinpointing affected population groups and suggests strategies to mitigate vaccination barriers and hesitations. Furthermore, we estimate potential cost savings resulting from the implementation of these strategies. To achieve our objectives, we employed Bayesian data mining methods to streamline data dimensions and identify significant variables (features) influencing vaccination decisions. Comparative analysis reveals that the Bayesian method outperforms cutting-edge alternatives, demonstrating superior performance.

Family Vulnerability Scale: validity evidence in primary health care.

OBJECTIVE: Investigate evidence of validity of the Family Vulnerability Scale (EVFAM-BR) as an instrument to support population-based management in primary health care (PHC), in the scope of Health Care Planning (PAS). METHODS: This is a psychometric study to assess any additional evidence of the internal structure of EVFAM-BR using confirmatory factor analysis (CFA) and network analysis (NA). A preliminary version of the scale with 38 items was submitted to patients of PHC facilities that use the PAS methodology, distributed across the five regions of Brazil. For the primary CFA data, factor loadings and predictive power (R2) of the item were used. Seven model adjustment indices were adopted and reliability was measured by three indicators, using Bayesian estimation. RESULTS: The preliminary version of the scale was applied to 1,255 patients. Using the AFC, factor loadings ranged from 0.66 to 0.90 and R2 from 0.44 to 0.81. Both the primary indicators and the model adequacy indices presented satisfactory and consistent levels. According to the NA, the items were appropriately associated with their peers, respecting the established dimensions, thus demonstrating sustainability and stability of the proposed model. CONCLUSIONS: The evidence of validity presented by EVFAM-BR indicates, for the first time in Brazil, a concise instrument that is able to assertively measure family vulnerability, potentially supporting population-based management.

Brazilian Scale for Evaluation of Mental Health Care Needs: Additional Evidence.

OBJECTIVE: To investigate validity evidence of the Brazilian Scale for Evaluation of Mental Health Care Needs (CuidaSM). METHODS: This is a psychometric study, which seeks additional evidence of internal structure. Data collection was carried out in 11 Primary Health Care (PHC) services , which implement the Health Care Planning (HCP) methodology, distributed across the five Brazilian regions. The preliminary version of CuidaSM, containing a block self-referred by the user and another block evaluated by PHC professionals, was applied to users aged 18 or over who attended the PHC services for consultation with a higher education professional. The techniques of confirmatory factor analysis and network analysis were used to investigate validity evidence. For the primary data of the confirmatory factor analysis, the factorial loads and the item's predictive power (R2) were used. Six model adjustment indices were adopted and reliability was measured by three indicators using Bayesian estimation. RESULTS: A total of 879 users participated in the study. By confirmatory factor analysis, factorial loads ranged from 0.43 to 0.99 and R2 from 0.19 to 0.98. Both the primary indicators and the model adequacy indices were established at satisfactory and consistent levels. The network analysis showed that the items were appropriately associated with their peers, respecting the established dimensions, which again indicates the sustainability and stability of the proposed model. CONCLUSIONS: The study findings confirm a consistent and reliable model of the instrument, through a combination of techniques. Considering the importance of using solid instruments in clinical practice, CuidaSM is a promising tool for population-based management and network care organization, aligned with HCP proposals.

'How to adapt forests?'-Exploring the role of leaf trait diversity for long-term forest biomass under new climate normals.

Forests, critical components of global ecosystems, face unprecedented challenges due to climate change. This study investigates the influence of functional diversity-as a component of biodiversity-to enhance long-term biomass of European forests in the context of changing climatic conditions. Using the next-generation flexible trait-based vegetation model, LPJmL-FIT, we explored the impact of functional diversity on long-term forest biomass under three different climate change scenarios (video abstract: https://www.pik-potsdam.de/~billing/video/2023/video_abstract_billing_et_al_LPJmLFIT.mp4). Four model set-ups were tested with varying degrees of functional diversity and best-suited functional traits. Our results show that functional diversity positively influences long-term forest biomass, particularly when climate warming is low (RCP2.6). Under these conditions, high-diversity simulations led to an approximately 18.2% increase in biomass compared to low-diversity experiments. However, as climate change intensity increased, the benefits of functional diversity diminished (RCP8.5). A Bayesian multilevel analysis revealed that both full leaf trait diversity and diversity of plant functional types contributed significantly to biomass enhancement under low warming scenarios in our model simulations. Under strong climate change, the presence of a mixture of different functional groups (e.g. summergreen and evergreen broad-leaved trees) was found more beneficial than the diversity of leaf traits within a functional group (e.g. broad-leaved summergreen trees). Ultimately, this research challenges the notion that planting only the most productive and climate-suited trees guarantees the highest future biomass and carbon sequestration. We underscore the importance of high functional diversity and the potential benefits of fostering a mixture of tree functional types to enhance long-term forest biomass in the face of climate change.

Phylodynamic analysis of foot-and-mouth disease virus evolution in Mar Chiquita, Argentina.

Foot-and-mouth disease is a highly contagious disease affecting cloven-hoofed animals, resulting in considerable economic losses. Its causal agent is foot-and-mouth disease virus (FMDV), a picornavirus. Due to its error-prone replication and rapid evolution, the transmission and evolutionary dynamics of FMDV can be studied using genomic epidemiological approaches. To analyze FMDV evolution and identify possible transmission routes in an Argentinean region, field samples that tested positive for FMDV by PCR were obtained from 21 farms located in the Mar Chiquita district. Whole FMDV genome sequences were obtained by PCR amplification in seven fragments and sequencing using the Sanger technique. The genome sequences obtained from these samples were then analyzed using phylogenetic, phylogeographic, and evolutionary approaches. Three local transmission clusters were detected among the sampled viruses. The dataset was analyzed using Bayesian phylodynamic methods with appropriate coalescent and relaxed molecular clock models. The estimated mean viral evolutionary rate was 1.17 × 10- 2 substitutions/site/year. No significant differences in the rate of viral evolution were observed between farms with vaccinated animals and those with unvaccinated animals. The most recent common ancestor of the sampled sequences was dated to approximately one month before the first reported case in the outbreak. Virus transmission started in the south of the district and later dispersed to the west, and finally arrived in the east. Different transmission routes among the studied herds, such as non-replicating vectors and close contact contagion (i.e., aerosols), may be responsible for viral spread.

Comparison of peritoneal interposition flaps and sealants for prevention of lymphocele after robotic radical prostatectomy and pelvic lymph node dissection: a systematic review, meta-analysis, Bayesian network meta-analysis, and meta-regression.

Lymphocele is one of the most common complications after radical prostatectomy. Multiple authors have proposed the use of vessel sealants or peritoneal interposition techniques as preventive interventions. This study aimed to aggregate and analyze the available literature on different interventions which seek to prevent lymphocele through a Bayesian Network. A systematic review was performed to identify prospective studies evaluating strategies for lymphocele prevention after robot assisted laparoscopic prostatectomy + pelvic lymph node dissection. Data was inputted into Review Manager 5.4 for pairwise meta-analysis. Data was then used to build a network in R Studio. These networks were used to model 200,000 Markov Chains via MonteCarlo sampling. The results are expressed as odds ratios (OR) with 95% credible intervals (CrI). Meta-regression was used to determine coefficient of change and adjust for pelvic lymph node dissection extent. Ten studies providing data from 2211 patients were included. 1097 patients received an intervention and 1114 patients served as controls. Interposition with fenestration had the lowest risk of developing a lymphocele (OR 0.14 [0.04, 0.50], p = 0.003). All interventions, except sealants or patches, had significant decreased odds of lymphocele rates. Meta-analysis of all the included studies showed a decreased risk of developing a lymphocele (OR 0.42 [0.33, 0.53], p < 0.00001) for the intervention group. Perivesical fixation and interposition with fenestration appear to be effective interventions for reducing the overall incidence of lymphocele.

Predicting Cellular Rejection of Renal Allograft Based on the Serum Proteomic Fingerprint.

Kidney transplantation is an essential medical procedure that significantly enhances the survival rates and quality of life for patients with end-stage kidney disease. However, despite advancements in immunosuppressive therapies, allograft rejection remains a leading cause of organ loss. Notably, predictions of cellular rejection processes primarily rely on biopsy analysis, which is not routinely performed due to its invasive nature. The present work evaluates if the serum proteomic fingerprint, as acquired by Fourier Transform Infrared (FTIR) spectroscopy, can predict cellular rejection processes. We analyzed 28 serum samples, corresponding to 17 without cellular rejection processes and 11 associated with cellular rejection processes, as based on biopsy analyses. The leave-one-out-cross validation procedure of a Naïve Bayes model enabled the prediction of cellular rejection processes with high sensitivity and specificity (AUC > 0.984). The serum proteomic profile was obtained in a high-throughput mode and based on a simple, rapid, and economical procedure, making it suitable for routine analyses and large-scale studies. Consequently, the current method presents a high potential to predict cellular rejection processes translatable to clinical scenarios, and that should continue to be explored.

Association between Multiple Trace Elements, Executive Function, and Cognitive Impairment with No Dementia in Older Adults.

Many studies suggest a significant association between individual essential trace elements (ETEs) and cognitive impairment in older adults, but evidence of the synchronized effect of multiple ETEs on cognitive function is lacking. We investigated the association between multiple ETEs, cognitive impairment with no dementia (CIND), and executive function in older Korean adults, using the Bayesian kernel machine regression (BKMR) model. Three hundred and thirty-six older adults were included as the study population and classified as the CIND and control groups. Blood manganese (Mn), copper (Cu), zinc (Zn), selenium (Se), and molybdenum (Mo) were measured as relevant ETEs. The frontal/executive tests included digit symbol coding (DSC), the Korean color word Stroop test (K-CWST), a controlled oral word association test (COWAT), and a trial-making test (TMT). Overall, the BKMR showed a negative association between multiple ETEs and the odds of CIND. Mn was designated as the most dominant element associated with the CIND (PIP = 0.6184), with a U-shaped relationship. Cu and Se levels were positively associated with the K-CWST percentiles (ß = 31.78; 95% CI: 13.51, 50.06) and DSC percentiles (ß = 25.10; 95% CI: 7.66, 42.53), respectively. Our results suggest that exposure to multiple ETEs may be linked to a protective mechanism against cognitive impairment in older adults.

Application of interpretable machine learning algorithms to predict distant metastasis in ovarian clear cell carcinoma.

BACKGROUND: Ovarian clear cell carcinoma (OCCC) represents a subtype of ovarian epithelial carcinoma (OEC) known for its limited responsiveness to chemotherapy, and the onset of distant metastasis significantly impacts patient prognoses. This study aimed to identify potential risk factors contributing to the occurrence of distant metastasis in OCCC. METHODS: Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, we identified patients diagnosed with OCCC between 2004 and 2015. The most influential factors were selected through the application of Gaussian Naive Bayes (GNB) and Adaboost machine learning algorithms, employing a Venn test for further refinement. Subsequently, six machine learning (ML) techniques, namely XGBoost, LightGBM, Random Forest (RF), Adaptive Boosting (Adaboost), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were employed to construct predictive models for distant metastasis. Shapley Additive Interpretation (SHAP) analysis facilitated a visual interpretation for individual patient. Model validity was assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the receiver operating characteristic curve (AUC). RESULTS: In the realm of predicting distant metastasis, the Random Forest (RF) model outperformed the other five machine learning algorithms. The RF model demonstrated accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) values of 0.792 (0.762-0.823), 0.904 (0.835-0.973), 0.759 (0.731-0.787), 0.221 (0.186-0.256), 0.974 (0.967-0.982), 0.353 (0.306-0.399), and 0.834 (0.696-0.967), respectively, surpassing the performance of other models. Additionally, the calibration curve's Brier Score (95%) for the RF model reached the minimum value of 0.06256 (0.05753-0.06759). SHAP analysis provided independent explanations, reaffirming the critical clinical factors associated with the risk of metastasis in OCCC patients. CONCLUSIONS: This study successfully established a precise predictive model for OCCC patient metastasis using machine learning techniques, offering valuable support to clinicians in making informed clinical decisions.

Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI.

Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creating machine-based forecasts. Machine learning and Explainable AI are crucial in classification as they not only provide accurate predictions but also offer insights into how the model arrives at its decisions, aiding in the understanding and trustworthiness of the classification results. In this study, we evaluate and compare the classification accuracy, precision, recall, and F1 scores of five different machine learning methods using a primary dataset (500 patients from Dhaka Medical College Hospital). Five different supervised machine learning techniques, including decision tree, random forest, logistic regression, naive bayes, and XGBoost, have been used to achieve optimal results on our dataset. Additionally, this study applied SHAP analysis to the XGBoost model to interpret the model's predictions and understand the impact of each feature on the model's output. We compared the accuracy with which several algorithms classified the data, as well as contrasted with other literature in this field. After final evaluation, this study found that XGBoost achieved the best model accuracy, which is 97%.

Control of false discoveries in grouped hypothesis testing for eQTL data.

BACKGROUND: Expression quantitative trait locus (eQTL) analysis aims to detect the genetic variants that influence the expression of one or more genes. Gene-level eQTL testing forms a natural grouped-hypothesis testing strategy with clear biological importance. Methods to control family-wise error rate or false discovery rate for group testing have been proposed earlier, but may not be powerful or easily apply to eQTL data, for which certain structured alternatives may be defensible and may enable the researcher to avoid overly conservative approaches. RESULTS: In an empirical Bayesian setting, we propose a new method to control the false discovery rate (FDR) for grouped hypotheses. Here, each gene forms a group, with SNPs annotated to the gene corresponding to individual hypotheses. The heterogeneity of effect sizes in different groups is considered by the introduction of a random effects component. Our method, entitled Random Effects model and testing procedure for Group-level FDR control (REG-FDR), assumes a model for alternative hypotheses for the eQTL data and controls the FDR by adaptive thresholding. As a convenient alternate approach, we also propose Z-REG-FDR, an approximate version of REG-FDR, that uses only Z-statistics of association between genotype and expression for each gene-SNP pair. The performance of Z-REG-FDR is evaluated using both simulated and real data. Simulations demonstrate that Z-REG-FDR performs similarly to REG-FDR, but with much improved computational speed. CONCLUSION: Our results demonstrate that the Z-REG-FDR method performs favorably compared to other methods in terms of statistical power and control of FDR. It can be of great practical use for grouped hypothesis testing for eQTL analysis or similar problems in statistical genomics due to its fast computation and ability to be fit using only summary data.

Plastid phylogenomics and fossil evidence provide new insights into the evolutionary complexity of the 'woody clade' in Saxifragales.

BACKGROUND: The "woody clade" in Saxifragales (WCS), encompassing four woody families (Altingiaceae, Cercidiphyllaceae, Daphniphyllaceae, and Hamamelidaceae), is a phylogenetically recalcitrant node in the angiosperm tree of life, as the interfamilial relationships of the WCS remain contentious. Based on a comprehensive sampling of WCS genera, this study aims to recover a robust maternal backbone phylogeny of the WCS by analyzing plastid genome (plastome) sequence data using Bayesian inference (BI), maximum likelihood (ML), and maximum parsimony (MP) methods, and to explore the possible causes of the phylogenetic recalcitrance with respect to deep relationships within the WCS, in combination with molecular and fossil evidence. RESULTS: Although the four WCS families were identically resolved as monophyletic, the MP analysis recovered different tree topologies for the relationships among Altingiaceae, Cercidiphyllaceae, and Daphniphyllaceae from the ML and BI phylogenies. The fossil-calibrated plastome phylogeny showed that the WCS underwent a rapid divergence of crown groups in the early Cretaceous (between 104.79 and 100.23 Ma), leading to the origin of the stem lineage ancestors of Altingiaceae, Cercidiphyllaceae, Daphniphyllaceae, and Hamamelidaceae within a very short time span (∼4.56 Ma). Compared with the tree topology recovered in a previous study based on nuclear genome data, cytonuclear discordance regarding the interfamilial relationships of the WCS was detected. CONCLUSIONS: Molecular and fossil evidence imply that the early divergence of the WCS might have experienced radiative diversification of crown groups, extensive extinctions at the genus and species levels around the Cretaceous/Paleocene boundary, and ancient hybridization. Such evolutionarily complex events may introduce biases in topological estimations within the WCS due to incomplete lineage sorting, cytonuclear discordance, and long-branch attraction, potentially impacting the accurate reconstruction of deep relationships.

Do beta-blockers reduce negative intrusive thoughts and anxiety in cancer survivors? - An emulated trial.

BACKGROUND: High rates of negative intrusive thoughts have been reported among cancer patients. Prevalent users of beta-blocker therapy have reported lower levels of cancer related intrusive thoughts than non-user. The aim of this study is to investigate if initiation of beta-blocker therapy reduces the prevalence and severity of intrusive thoughts (co-primary endpoints) and the prevalence of anxiety, depressed mood, and low quality of life (secondary endpoints) in cancer survivors. METHODS: Data on patient-reported outcomes from three cohort studies of Swedish patients diagnosed with colon, prostate or rectal cancer were combined with data on beta-blocker prescriptions retrieved from the Swedish Prescribed Drug Register. Two randomized controlled trials were emulated. Trial 1 had follow-up 1 year after diagnosis, trial 2 had follow-up 2 years after diagnosis, baseline in both trials was 12 months before follow-up. Those who initiated beta-blocker therapy between baseline and follow-up was assigned Active group, those who did not was assigned Control group. All endpoints were analysed using Bayesian ordered logistic regression. RESULTS: Trial 1 consisted of Active group, n = 59, and Control group, n = 3936. Trial 2 consisted of Active group, n = 87, and Control group, n = 3132. The majority of participants were men, 83% in trial 1 and 94% in trial 2. The prevalence and severity of intrusive thoughts were lower in the Active group in trial 1, but no significant differences between groups were found in either trial. The prevalence of depressed mood, worse quality of life and periods of anxiety were higher in the Active group in both trials with significant differences for quality of life in trial 1 and anxiety in trial 2. CONCLUSIONS: The emulated trials demonstrated no evidence of a protective effect of beta-blocker therapy against intrusive thoughts. The Active group had reduced quality of life and elevated anxiety compared to the Control group. TRIAL REGISTRATION: The three cohort studies were registered at isrctn.com/clinicaltrials.gov (ISRCTN06393679, NCT02530593 and NCT01477229).

How Network Structure Shapes Languages: Disentangling the Factors Driving Variation in Communicative Agents.

Languages show substantial variability between their speakers, but it is currently unclear how the structure of the communicative network contributes to the patterning of this variability. While previous studies have highlighted the role of network structure in language change, the specific aspects of network structure that shape language variability remain largely unknown. To address this gap, we developed a Bayesian agent-based model of language evolution, contrasting between two distinct scenarios: language change and language emergence. By isolating the relative effects of specific global network metrics across thousands of simulations, we show that global characteristics of network structure play a critical role in shaping interindividual variation in language, while intraindividual variation is relatively unaffected. We effectively challenge the long-held belief that size and density are the main network structural factors influencing language variation, and show that path length and clustering coefficient are the main factors driving interindividual variation. In particular, we show that variation is more likely to occur in populations where individuals are not well-connected to each other. Additionally, variation is more likely to emerge in populations that are structured in small communities. Our study provides potentially important insights into the theoretical mechanisms underlying language variation.

Determinants of multimodal fake review generation in China's E-commerce platforms.

This paper develops a theoretical model of determinants influencing multimodal fake review generation using the theories of signaling, actor-network, motivation, and human-environment interaction hypothesis. Applying survey data from users of China's three leading E-commerce platforms (Taobao, Jingdong, and Pinduoduo), we adopt structural equation modeling, machine learning technique, and Bayesian complex networks analysis to perform factor identification, path analysis, feature factor importance ranking, regime division, and network centrality analysis of full sample, male sample, and female sample to reach the following conclusions: (1) platforms' multimodal recognition and governance capabilities exert significant negative moderating effects on merchants' information behavior, while it shows no apparent moderating effect on users' information behavior; users' emotional venting, perceived value, reward mechanisms, and subjective norms positively influence multimodal fake review generation through perceptual behavior control; (2) feature factors of multimodal fake review generation can be divided into four regimes, i.e., regime 1 includes reward mechanisms and perceived social costs, indicating they are key feature factors of multimodal fake review generation; merchant perception impact is positioned in regime 2, signifying its pivotal role in multimodal fake review generation; regime 3 includes multimodal recognition and governance capabilities, supporting/disparaging merchants, and emotional venting; whereas user perception impact is positioned in regime 4, indicating its weaker influence on multimodal fake review generation; (3) both in full sample, male sample, and female sample, reward mechanisms play a crucial role in multimodal fake review generation; perceived value, hiring review control agency, multimodal recognition and governance capabilities exhibit a high degree of correlation; however, results of network centrality analysis also exhibit heterogeneity between male and female samples, i.e., male sample has different trends in closeness centrality values and betweenness centrality values than female sample. This indicates that determinants influencing multimodal fake review generation are complex and interconnected.

The complete Chloroplast genome of Stachys geobombycis and comparative analysis with related Stachys species.

Herb genomics, at the forefront of traditional Chinese medicine research, combines genomics with traditional practices, facilitating the scientific validation of ancient remedies. This integration enhances public understanding of traditional Chinese medicine's efficacy and broadens its scope in modern healthcare. Stachys species encompass annual or perennial herbs or small shrubs, exhibiting simple petiolate or sessile leaves. Despite their wide-ranging applications across various fields, molecular data have been lacking, hindering the precise identification and taxonomic elucidation of Stachys species. To address this gap, we assembled the complete chloroplast (CP) genome of Stachys geobombycis and conducted reannotation and comparative analysis of seven additional species within the Stachys genus. The findings demonstrate that the CP genomes of these species exhibit quadripartite structures, with lengths ranging from 14,523 to 150,599 bp. Overall, the genome structure remains relatively conserved, hosting 131 annotated genes, including 87 protein coding genes, 36 tRNA genes, and 8 rRNA genes. Additionally, 78 to 98 SSRs and long repeat sequences were detected , and notably, 6 highly variable regions were identified as potential molecular markers in the CP genome through sequence alignment. Phylogenetic analysis based on Bayesian inference and maximum likelihood methods strongly supported the phylogenetic position of the genus Stachys as a member of Stachydeae tribe. Overall, this comprehensive bioinformatics study of Stachys CP genomes lays the groundwork for phylogenetic classification, plant identification, genetic engineering, evolutionary studies, and breeding research concerning medicinal plants within the Stachys genus.

Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models.

Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images. Real fine-needle aspiration (FNA) images were collected from Wisconsin, and four classifiers were used, including three machine learning models and one regression model: the support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (k-NN), and decision tree (DT)-C4.5. According to the accuracy, sensitivity, and specificity results, the SVM algorithm had the best performance; it was the most powerful computational classifier with a 97.13% accuracy and 97.5% specificity. It also had around a 96% sensitivity for the diagnosis of breast cancer, unlike the models used for comparison, thereby providing an exact diagnosis on the one hand and a clear classification between benign and malignant tumors on the other hand. As a future research prospect, more algorithms and combinations of features can be considered for the precise, rapid, and effective classification and diagnosis of breast cancer images for imperative decisions.

[Comparative analysis of plastomes of Rubus chingii and R. chingii var. suavissimus].

Rubus chingii and R. chingii var. suavissimus are unique dual-purpose plant resources, with significant nutraceutical, pharmaceutical, and economic value, as well as promising prospects for further development. To investigate the genetic structure and evolutionary characteristics of these two varieties, this study conducted plastome sequencing using the Illumina HiSeq XTen sequencing platform. Subsequently, the study performed assembly, annotation, and characterization of the genomes, followed by a comparative plastome and phylogenetic analysis using bioinformatics techniques. The results revealed that the plastomes of R. chingii and R. chingii var. suavissimus exhibited a tetrad structure, comprising a large single-copy region(LSC), a small single-copy region(SSC), and two inverted repeat regions(IRs). The study identified a total of 56 simple sequence repeats(SSRs) after comparative analysis, predominantly consisting of A and T. Furthermore, the structure of the IR boundary genes in both varieties was found to be highly conserved, with only minor nucleotide variations. Additionally, the study identified three highly variable regions: rps16-trnQ-psbK, trnR-atpA, and trnT-trnL, which held promise as potential identification marks for further development and utilization. Phylogenetic analysis results obtained by the maximum likelihood and Bayesian inference methods demonstrated a close clustering of R. chingii and R. chingii var. suavissimus(100% support), with their closest relatives being R. trianthus. This study, focusing on plastome-level genetic distinctions between these two varieties, lays a foundation for future species protection, development, and utilization.

Efficacy of immune checkpoint inhibitors in advanced non-small cell lung cancer patients with KRAS mutations: A network meta-analysis.

OBJECTIVE: Previous studies have shown that immune checkpoint inhibitors can improve the survival of patients with advanced non-small cell lung cancer with KRAS mutations; however, there is a lack of comparisons between treatment regimens associated with immune checkpoint inhibitors, and our study aims to compare several treatment parties to find a more effective treatment regimen. METHOD: A comprehensive literature search was conducted across multiple databases, namely PubMed, Web of Science, Embase, and Cochrane Library, to identify relevant studies. The screened studies were thoroughly examined, and data were collected to establish a Bayesian framework. The study focused on two primary endpoints: overall survival (OS) and progression-free survival (PFS). Data analysis and graphical plotting using R software and Revman (version 5.3). It is worth mentioning that the study protocol was registered with the International Prospective Registry for Systematic Reviews, ensuring transparency and adherence to predetermined protocols (CRD42022379595). RESULT: In total, our analysis included six RCTs involving 469 patients with KRAS mutations. Among these patients, 224 received chemotherapy, while 245 were treated with immune checkpoint inhibitors. Meta-analysis results showed that the addition of ICIs could significantly improve OS and PFS (0.69, 95% CI 0.55, 0.86; 0.57, 95% CI 0.42, 0.77). The results of the network meta-analysis showed that Pembrolizumab could improve OS (HR 0.42, 95% CI 0.22-0.80) and Pembrolizumab emerged as the most effective treatment option for enhancing OS in patients (SUCRA 65.03%). Additionally, pembrolizumab in combination with chemotherapy showed improvement in PFS (HR 0.47, 95% CI 0.29-0.76). CONCLUSION: Our analysis found that among advanced NSCLC patients with KRAS gene mutations, first-line treatment with pembrolizumab alone demonstrated greater efficacy. Similarly, second-line treatment with nivolumab alone was found to be more effective in this patient population. However, the sample size of this study was limited, Therefore, additional clinical data is necessary to validate this finding in subsequent research.

Six complete mitochondrial genomes of ground beetles from the Harpalinae and Carabinae (Coleoptera, Carabidae) with phylogenetic analysis based on mitogenomic data.

In this study, we employed high-throughput sequencing technology to determine the complete mitochondrial genomes of six ground beetles, encompassing five Harpalinae species and one Carabinae species. The sizes of mitochondrial genomes ranged from 15,334 to 16,972 bp, encompassing 37 genes, including 13 protein-coding genes, 22 transfer RNA genes, and 2 ribosomal RNA genes. Furthermore, each species was found to possess a putative control region. Combining with 65 published mitochondrial genome sequences of Carabidae as ingroups and four species from Trachypachidae, Gyrinidae and Dytiscidae as outgroups, we conducted phylogenetic analyses utilizing Maximum likelihood and Bayesian inference methods. Moreover, we reconstructed a species tree of Carabidae based on mitochondrial genome data using the coalescent-based species tree method (ASTRAL). The results revealed that the family Carabidae was not a monophyletic group. The subfamily Harpalinae was supported to be a monophyletic group in Maximum likelihood analysis. Although the subfamily Carabinae was found to be nonmonophyletic in the concatenation analyses under both Maximum likelihood and Bayesian inference criteria, it was identified as a monophyletic group in the species tree analysis.