Prevalence Of Type Research Articles

Exploring associations between accident types and activities in construction using natural language processing

Existing accident analysis methods often fail to integrate the relationship of construction accidents with construction activities, restrict the potential for visualizing the accidents frequency in Building Information Modelling (BIM). This study proposes a decision support system that provides visual insights, and the frequency of the core construction accidents in relation to typical construction activities. K-mean clustering and keyword-matching techniques are used to group the leading causes of construction accidents and then compare them with the construction activities. The average hit rate computed for construction accidents was 91%, whereas the hit rate calculated for construction activities connected to these frequent accidents was 75%. The classification models are integrated into the Power BI platform to offer decision-makers deeper insights regarding the relationship of prevalent construction accident types and associated activities. The practical application of the system is demonstrated with a case study, exemplifying the fall accident data integrated in a BIM environment.

The effect of a first occurrence of digital dermatitis in early lactation on risk of culling and time-to-pregnancy in lactating dairy cows

Digital dermatitis (DD) is one of the most prevalent types of hoof lesion reported in dairy cows. This higher prevalence in dairy herds is consequential, as DD can not only lead be painful and lead to lameness in severe instance, but DD has also been found to have a negative long-term impact on reproductive per­formance and longevity. However, the impact of a first instance of DD in cows with no previous lesion history on these negative outcomes is largely unknown, as most previous studies do not account or the impact of lesion chronicity. The objective of this study was to evaluate the effect of a first occurrence of DD in the first 60 days in milk (DIM) in cows with no previous record­ed instance of DD on risk of culling and time-to-pregnancy in the first 180 DIM in dairy cows.

Surviving a Double-Edged Sword: Response of Horticultural Crops to Multiple Abiotic Stressors

Climate change-induced weather events, such as extreme temperatures, prolonged drought spells, or flooding, pose an enormous risk to crop productivity. Studies on the implications of multiple stresses may vary from those on a single stress. Usually, these stresses coincide, amplifying the extent of collateral damage and contributing to significant financial losses. The breadth of investigations focusing on the response of horticultural crops to a single abiotic stress is immense. However, the tolerance mechanisms of horticultural crops to multiple abiotic stresses remain poorly understood. In this review, we described the most prevalent types of abiotic stresses that occur simultaneously and discussed them in in-depth detail regarding the physiological and molecular responses of horticultural crops. In particular, we discussed the transcriptional, posttranscriptional, and metabolic responses of horticultural crops to multiple abiotic stresses. Strategies to breed multi-stress-resilient lines have been presented. Our manuscript presents an interesting amount of proposed knowledge that could be valuable in generating resilient genotypes for multiple stressors.

Advances in Therapeutics to Alleviate Cognitive Decline and Neuropsychiatric Symptoms of Alzheimer’s Disease

Dementia exists as a ‘progressive clinical syndrome of deteriorating mental function significant enough to interfere with activities of daily living’, with the most prevalent type of dementia being Alzheimer’s disease (AD), accounting for about 80% of diagnosed cases. AD is associated with an increased risk of comorbidity with other clinical conditions such as hypertension, diabetes, and neuropsychiatric symptoms (NPS) including, agitation, anxiety, and depression as well as increased mortality in late life. For example, up to 70% of patients diagnosed with AD are affected by anxiety. As aging is the major risk factor for AD, this represents a huge global burden in ageing populations. Over the last 10 years, significant efforts have been made to recognize the complexity of AD and understand the aetiology and pathophysiology of the disease as well as biomarkers for early detection. Yet, earlier treatment options, including acetylcholinesterase inhibitors and glutamate receptor regulators, have been limited as they work by targeting the symptoms, with only the more recent FDA-approved drugs being designed to target amyloid-β protein with the aim of slowing down the progression of the disease. However, these drugs may only help temporarily, cannot stop or reverse the disease, and do not act by reducing NPS associated with AD. The first-line treatment options for the management of NPS are selective serotonin reuptake inhibitors/selective noradrenaline reuptake inhibitors (SSRIs/SNRIs) targeting the monoaminergic system; however, they are not rational drug choices for the management of anxiety disorders since the GABAergic system has a prominent role in their development. Considering the overall treatment failures and side effects of currently available medication, there is an unmet clinical need for rationally designed therapies for anxiety disorders associated with AD. In this review, we summarize the current status of the therapy of AD and aim to highlight novel angles for future drug therapy in our ongoing efforts to alleviate the cognitive deficits and NPS associated with this devastating disease.

Long non-coding RNA MSL3P1 regulates CUL3 mRNA cytoplasmic transport and stability and promotes lung adenocarcinoma metastasis.

Lung adenocarcinoma (LUAD) is the most prevalent histological type of lung cancer. Previous studies have reported that specific long non-coding RNAs (lncRNAs) are involved in cancer development and progression. The phenotype and mechanism of ENST00000440028, named MSL3P1, a lncRNA which we referring to a cancer-testis gene with potential roles in tumorigenesis and progression, have not been reported. We found that MSL3P1 is overexpressed in LUAD tumor tissues, which is significantly associated with clinical characteristics, metastasis, and poor clinical prognosis. MSL3P1 promotes the metastasis of LUAD in vitro and in vivo. The enhancer reprogramming in LUAD tumor tissue is the major driver of the aberrantly expression of MSL3P1. Mechanistically, due to the competitive binding to CUL3 mRNA with ZFC3H1 protein (a protein involved in targeting polyadenylated RNA to exosomes and promoting the degradation of target mRNA), MSL3P1 can prevent the ZFC3H1-mediated RNA degradation of CUL3 mRNA and transport it to the cytoplasm. This activates the downstream epithelial-to-mesenchymal transition signaling pathway, and promote tumor invasion and metastasis. Implications: This study indicates that lncRNA MSL3P1 regulates CUL3 mRNA stability and promotes the metastasis and holds potential as a prognostic biomarker and therapeutic target in LUAD.

Decoding Qur'anic Linguistics through Fi'il Tsulasi Mazid in Surah Az-Zukhruf

This study examines the occurrences and benefits of fi'il tsulasi mazid (trilateral verbs with additional letters) in Surah Az-Zukhruf, focusing on Arabic language learning and Qur'anic comprehension. Through a detailed analysis, we identified 57 instances of these verbs and categorized them based on their forms and types. Results indicate that the most common verb types are af'ala yuf'ilu and ifta'ala yafta'ilu, while others include fa'ala yufa'ilu and faa'ala yufaa'ilu. Various benefits of these verb forms, such as litta'diyah, littikhad, and littahawul, were identified. This research provides valuable insights for Arabic language educators, allowing them to better explain linguistic changes and improve students' understanding of Arabic as the language of the Qur'an. Hightligh: 1. Detailed Analysis: Studying 57 instances of fi'il tsulasi mazid.2. Common Verb Types: Identifying prevalent verb types.3. Educational Implications: Enhancing Arabic language teaching for Qur'anic comprehension. Keywords: Arabic linguistics, Qur'an, Fi'il tsulasi mazid, Surah Az-Zukhruf, Language education

Mume Fructus reduces interleukin-1 beta-induced cartilage degradation via MAPK downregulation in rat articular chondrocytes.

Osteoarthritis is the most prevalent type of degenerative arthritis. It is characterized by persistent pain, joint dysfunction, and physical disability. Pain relief and inflammation control are prioritised during osteoarthritis treatment Mume Fructus (Omae), a fumigated product of the Prunus mume fruit, is used as a traditional medicine in several Asian countries. However, its therapeutic mechanism of action and effects on osteoarthritis and articular chondrocytes remain unknown. In this study, we analyzed the anti-osteoarthritis and articular regenerative effects of Mume Fructus extract on rat chondrocytes. Mume Fructus treatment reduced the interleukin-1β-induced expression of matrix metalloproteinase 3, matrix metalloproteinase 13, and a disintegrin and metalloproteinase with thrombospondin type 1 motifs 5. Additionally, it enhanced collagen type II alpha 1 chain and aggrecan accumulation in rat chondrocytes. Furthermore, Mume Fructus treatment regulated the inflammatory cytokine levels, mitogen-activated protein kinase phosphorylation, and nuclear factor-kappa B activation. Overall, our results demonstrated that Mume Fructus inhibits osteoarthritis progression by inhibiting the nuclear factor-kappa B and mitogen-activated protein kinase pathways to reduce the levels of inflammatory cytokines and prevent cartilage degeneration. Therefore, Mume Fructus may be a potential therapeutic option for osteoarthritis.

Critical Risk Assessment, Diagnosis, and Survival Analysis of Breast Cancer

Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epidemiology, and End Results) Breast Cancer Dataset. Applying resampling techniques on the training dataset before using various machine learning techniques can affect the performance of the classifiers. The three breast cancer datasets were examined using a variety of pre-processing approaches and classification models to assess their performance in terms of accuracy, precision, F-1 scores, etc. The PCA (principal component analysis) and resampling strategies produced remarkable results. For the BCSC Dataset, the Random Forest algorithm exhibited the best performance out of the applied classifiers, with an accuracy of 87.53%. Out of the different resampling techniques applied to the training dataset for training the Random Forest classifier, the Tomek Link exhibited the best test accuracy, at 87.47%. We compared all the models used with previously used techniques. After applying the resampling techniques, the accuracy scores of the test data decreased even if the training data accuracy increased. For the Breast Cancer Wisconsin diagnostic dataset, the K-Nearest Neighbor algorithm had the best accuracy with the original dataset test set, at 94.71%, and the PCA dataset test set exhibited 95.29% accuracy for detecting breast cancer. Using the SEER Dataset, this study also explores survival analysis, employing supervised and unsupervised learning approaches to offer insights into the variables affecting breast cancer survivability. This study emphasizes the significance of individualized approaches in the management and treatment of breast cancer by incorporating phenotypic variations and recognizing the heterogeneity of the disease. Through data-driven insights and advanced machine learning, this study contributes significantly to the ongoing efforts in breast cancer research, diagnostics, and personalized medicine.

Construction of exosome-related genes risk model in kidney cell carcinoma predicts prognosis and immune therapy response.

Renal cell carcinoma (RCC) is one of the most prevalent types of urological cancer. Exosomes are vesicles derived from cells and have been found to promote the development of RCC, but the potential biomarker and molecular mechanism of exosomes on RCC remain ambiguous. Here, we first screened differentially expressed exosome-related genes (ERGs) by analyzing The Cancer Genome Atlas (TCGA) database and exoRBase 2.0 database. We then determined prognosis-related ERGs (PRERGs) by univariate Cox regression analysis. Gene Dependency Score (gDS), target development level, and pathway correlation analysis were utilized to examine the importance of PRERGs. Machine learning and lasso-cox regression were utilized to screen and construct a 5-gene risk model. The risk model showed high predictive accuracy for the prognosis of patients and proved to be an independent prognostic factor in three RCC datasets, including TCGA-KIRC, E-MTAB-1980, and TCGA-KIRP datasets. Patients with high-risk scores showed worse outcomes in different clinical subgroups, revealing that the risk score is robust. In addition, we found that immune-related pathways are highly enriched in the high-risk group. Activities of immune cells were distinct in high-/low-risk groups. In independent immune therapeutic cohorts, high-risk patients show worse immune therapy responses. In summary, we identified several exosome-derived genes that might play essential roles in RCC and constructed a 5-gene risk signature to predict the prognosis of RCC and immune therapy response.

A comparative analysis of alternative splicing patterns in Atlantic salmon (Salmo salar) in response to Moritella viscosa and sea lice (Lepeophtheirus salmonis) infection

Moritella viscosa (M. viscosa) and sea lice (Lepeophtheirus salmonis) are severe pathogens that primarily infect the skin of Atlantic salmon (Salmo salar), which cause significant economic losses in the farming industry. However, the pathogenesis and molecular mechanisms underlying the host's immune defence at the post-transcriptional level remain unclear. Alternative splicing (AS) is an evolutionarily conserved post-transcriptional mechanism that can greatly increase the richness of the transcriptome and proteome. In this study, transcriptomic data derived from skin tissues of Atlantic salmon after M. viscosa and sea lice infections were used to examine the AS profiles and their differential expression patterns. In total, we identified 33,044 AS events (involving 13,718 genes) in the control (CON) group, 35,147 AS events (involving 14,340 genes) in the M. viscosa infection (MV) group, and 30,364 AS events (involving 13,142 genes) in the sea lice infection (LC) group, respectively. Among the five types of AS identified in our study (i.e., SE, A5SS, A3SS, MXE, and RI), SE was the most prevalent type in all three groups (i.e., CON, MV, and LC groups). Decreased percent-spliced-in (PSI) levels were observed in SE events under both MV- and LC-infected conditions, suggesting that MV or LC infection elevated exon-skipping isoforms and promoted the selection of shorter transcripts in numerous DAS genes. In addition, most of the differential AS genes were found to be associated with pathways related to mRNA regulation, epithelial or muscle development, and immune response. These findings provide novel insights into the role of AS in host-pathogen interactions and represent the first comparative analysis of AS in response to bacterial and parasitic infections in fish.

Cu thin film growth on stepped Si substrate: Effects of incident energy and thermal annealing

The existence of defects on the substrate surface is unavoidable and has a significant impact on the determination of surface properties. Among the prevalent types of defects, steps play an important role. In this study, molecular dynamics simulations are employed to explore the growth process of a thin film of Cu on a Si (001) substrate with a step. This research investigates how incident energy and thermal annealing affect the growth mode, surface roughness, film-substrate interface, and dislocation evolution. The results indicate that during the initial stage of the growth, the nucleation phenomenon was observed on terraces. The simulation indicates that the Cu atoms occur in an island growth mode on the stepped substrate. It is found that when the incident energy is increased and thermal annealing is performed, the surface curvature and roughness are reduced. Furthermore, the intermixing thickness at the interface increases due to the variation of the incidence energy. A dislocation analysis revealed that some dislocations disappeared before and after annealing, and a perfect dislocation appeared by increasing the incident energy.

Toxicological Effects of Commonly Used Mosquito Coil Smoke on Liver and Lung Function in Rats

The utilization of mosquito coils as a household-level strategy for mosquito control is widely practiced in malaria-endemic regions spanning Africa, Asia, and South America. However, the continuous application of mosquito coils has prompted significant apprehensions concerning environmental and health implications. This research aims to evaluate the potential hepatic and pulmonary toxicities associated with the inhalation of mosquito coil smoke in rat subjects over both sub-chronic and chronic durations. A questionnaire was used to make inquiries regarding the prevalent types and brands of mosquito coils employed, the manner in which they are used, the durations for which they are utilized. A total of twenty-four albino rats were categorized into three distinct groups: the control group and two exposed groups. These groups were subjected to distinct brands of mosquito coil smoke across two distinct exposure regimens – a sub-chronic exposure lasting 4 weeks and a chronic exposure spanning 12 weeks. Following the exposure periods, blood samples were collected for the analysis of serum-based and biochemical markers, including serum aspartate aminotransferase (AST), alanine aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), bilirubin, serum bicarbonate, and Lactate dehydrogenase (LDH), oxygen saturation, C-reactive protein, total protein, and microscopic analysis of lung tissue and liver tissue. The histopathological analysis of lung tissue was appropriately included. However, the analysis of liver tissue was omitted. The results demonstrated a significant elevation in the levels of these biochemical markers among the exposed rats in comparison to the control group. Concurrently, a decrease in blood oxygen saturation was noted in the exposed rats when contrasted with the control group. Conversely, the serum concentrations of high-sensitivity C-reactive protein, total protein, and alanine aminotransferase (ALT) did not exhibit notable differences in relation to the control group. Additionally, microscopic analysis of lung tissue indicated the presence of histopathological lesions in the exposed rats, suggesting a detrimental impact of mosquito coil smoke exposure in a time-dependent progression.

Analyzing Writing Errors on Narrative Texts Made by Grade Nine Students’ of SMP IT Qurrata A’yun Palu

Students often struggle with grammatical accuracy when writing in English as a second language. This study investigates the types and frequencies of errors made by ninth-grade Indonesian students in their English narrative compositions. The research objectives were to 1) identify common error categories within student-generated narrative texts and 2) determine which error type occurs most frequently. The study utilized a descriptive qualitative approach. Ninth-grade students at SMP IT Qurata’Ayun Palu were assigned a narrative writing task on a designated topic. The collected texts were analyzed using Dulay, Burt, and Krashen's (1982) surface approach error taxonomy. Results indicated a total of 70 errors across the students' work. The most prevalent error type was misformation (50%), followed by omission (36%), addition (13%), and misordering (1%). These findings suggest that difficulties applying correct grammatical forms are a significant obstacle to accurate narrative writing for these students.

Cellular senescence induced by down-regulation of PTBP1 correlates with exon skipping of mitochondrial related gene NDUFV3

Abstract As the most prevalent type of alternative splicing in animal cells, exon skipping plays an important role in expanding the diversity of transcriptome and proteome, thereby participating the regulation of diverse physiological and pathological processes such as development, aging and cancer. Cellular senescence serving as an anti-cancer mechanism could also contribute to individual aging. Although the dynamic changes of exon skipping during cellular senescence were revealed, its biological consequence and upstream regulator remain poorly understood. Here, by using human foreskin fibroblasts (HFF) replicative senescence as a model, we discovered that splicing factor PTBP1 was an important contributor for global exon skipping events during senescence. Down-regulated expression of PTBP1 induced senescence-associated phenotypes and related mitochondrial functional changes. Mechanistically, PTBP1 binds to the third exon of mitochondrial complex I subunit coding gene NDUFV3 and protects the exon from skipping. We further confirmed that exon skipping of NDUFV3 correlates with and partially contributes to cellular senescence and related mitochondrial functional changes upon PTBP1 knockdown. Together, we revealed for the first time that mitochondrial related gene NDUFV3 is a new downstream target for PTBP1-regulated exon skipping to mediate cellular senescence and mitochondrial functional changes.

Detection of Oncogene Hotspot Mutations in Female NSCLC Tumor DNA and Cell-Free DNA.

Non-small-cell lung cancer (NSCLC) is the most prevalent type of lung cancer, with extensively characterized mutational spectra. Several biomarkers (such as EGFR, BRAF, KRAS gene mutations, etc.) have emerged as predictive and prognostic markers for NSCLC. Unfortunately, the quality of the available tumor biopsy and/or cytology material is not always adequate to perform the necessary molecular testing, prompting the search for alternatives. Cell-free DNA (cfDNA) found in plasma is emerging as a highly promising avenue or a supplementary method for assessing the efficacy of cancer treatments. This is especially valuable in instances where conventional biopsy specimens, like formalin-fixed, paraffin-embedded (FFPE), or freshly frozen tumor tissues prove inadequate for conducting molecular pathology analyses subsequent to the initial diagnostic procedures. By leveraging cfDNA from plasma, clinicians gain an additional tool to gauge the effectiveness of cancer therapies, thereby enhancing their ability to optimize tailored treatment strategies. In this study, 51 Lithuanian females with NSCLC were analyzed, with adenocarcinoma being the predominant pathology diagnosis in 40 cases (78%). Target mutations were identified in 38 out of 51 patients (74.5%) in tumor tissue samples, while in plasma samples, they were identified in only 10 patients' samples (19.6%). Even though we did not have enough voluminous plasma samples in our study, gene mutations were detected in plasma from ten women, three of whom were diagnosed with early stages of lung cancer (stages I and II). For these patients, the following mutations were detected: deletion in exon 19 of the EGFR gene and single nucleotide polymorphisms in the TP53 and MET genes. All other women were diagnosed with stages III or IV of lung cancer. This indicates that the later stages of cancer contribute more cfDNA in plasma, making extraction less complicated.

Analyzing the expression and clinical significance of CENPE in gastric cancer

BackgroundGastric cancer (GC) is a prevalent type of malignant gastrointestinal tumor. Many studies have shown that CENPE acts as an oncogene in some cancers. However, its expression level and clinical value in GC are not clear.MethodsObtaining clinical data information on gastric adenocarcinoma from TCGA and GEO databases. The gene expression profiling interaction analysis (GEPIA) was used to evaluate the relationship between prognosis and CENPE expression in gastric cancer patients. Utilizing the UALCAN platform, the correlation between CENPE expression and clinical parameters was examined. Functions and signaling pathways of CENPE were analyzed using the Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). The association between immunological infiltrating cells and CENPE expression was examined using TIMER2.0. Validation was performed by real-time quantitative PCR (qPT-PCR) and immunohistochemical analysis.ResultsAccording to the analysis of the GEPIA database, the expression of CENPE is increased in gastric cancer tissues compared to normal tissues. It was also found to have an important relationship with the prognosis of the patient (p<0.05). The prognosis was worse and overall survival was lower in individuals with increased expression of CENPE. In line with the findings of the GEPIA, real-time fluorescence quantitative PCR (qPT-PCR) confirmed that CENPE was overexpressed in gastric cancer cells. Furthermore, It was discovered that H. pylori infection status and tumor grade were related to CENPE expression. Enrichment analysis revealed that CENPE expression was linked to multiple biological functions and tumor-associated pathways. CENPE expression also correlated with immune-infiltrating cells in the gastric cancer microenvironment and was positively connected to NK cells and mast cells. According to immunohistochemical examination, paracancerous tissues had minimal expression of CENPE, but gastric cancer showed significant expression of the protein.ConclusionsAccording to our findings, CENPE is substantially expressed in GC and may perhaps contribute to its growth. CENPE might be a target for gastric cancer therapy and a predictor of a bad prognosis.

Towards High-Efficiency Buildings for Sustainable Energy Transition: Standardized Prefabricated Solutions for Roof Retrofitting

Enhancing energy efficiency in buildings plays a pivotal role in realizing the ambitious objective of achieving carbon neutrality by 2050, as outlined in the European Green Deal. Roofs represent the technical element most affected by energy phenomena related to heat transfer: in winter, roofing can lose up to 35% of heat, and the summer heat flux can even be higher. This paper provides a catalogue of optimized and sustainable solutions, with a specific focus on standardization and prefabrication principles, for enhancing the energy efficiency of the most prevalent types of roofs that characterize the national residential building heritage. The methodological approach that guided the research presented in this article was based on the identification and study of the most common roofings in the diverse national residential building heritage, followed by their classification according to their construction era. In the context of essential energy retrofitting of deteriorated residential building stock, 21 optimized standardized solutions have been identified. The outcome of performance evaluations of the proposed solutions allowed the implementation of a matrix that can be a valuable support for designers in selecting the most efficient precalculated and prefabricated solutions for the national residential building heritage based on energy performance and sustainability criteria.

FRAUD DETECTION AND PREVENTION IN FINANCIAL INSTITUTIONS

Financial fraud, characterized by deceptive practices aimed at securing financial gains, has emerged as a prevalent threat within companies and organizations. Traditional methods like manual verification and inspection are inaccurate, expensive, and time-consuming in identifying fraudulent activities. With the rise of artificial intelligence, machine learning-based approaches offer a promising solution to detect fraudulent transactions through the analysis of vast financial datasets. This paper presents a systematic literature review (SLR) that systematically surveys and consolidates existing literature on machine learning (ML)-based fraud detection. Employing the Kitchenham approach, which employs defined protocols to extract and synthesize relevant articles, this review reports the findings from various studies gathered through specified search strategies from electronic databases. After applying inclusion/exclusion criteria, 93 articles were selected, synthesized, and analyzed. The review outlines popular ML techniques utilized for fraud detection, prevalent fraud types, and evaluation metrics. The surveyed articles indicate that support vector machine (SVM) and artificial neural network (ANN) are commonly employed ML algorithms for fraud detection, with credit card fraud being the most addressed fraud type using ML techniques. Additionally, the paper discusses key issues, gaps, and limitations in financial fraud detection and suggests potential areas for future research. Furthermore, the review identifies challenges like class imbalance in datasets and the dynamic nature of fraud patterns. While SVM and ANN are popular, exploring ensemble methods, deep learning, and anomaly detection techniques shows promise for improving detection accuracy in suitability. Future research should address these limitations and foster collaboration between academia, industry, and regulatory bodies to advance fraud detection methods and safeguard financial transactions.

Gastric cancer actionable genomic alterations across diverse populations worldwide and pharmacogenomics strategies based on precision oncology

Introduction: Gastric cancer is one of the most prevalent types of cancer worldwide. The World Health Organization (WHO), the International Agency for Research on Cancer (IARC), and the Global Cancer Statistics (GLOBOCAN) reported an age standardized global incidence rate of 9.2 per 100,000 individuals for gastric cancer in 2022, with a mortality rate of 6.1. Despite considerable progress in precision oncology through the efforts of international consortia, understanding the genomic features and their influence on the effectiveness of anti-cancer treatments across diverse ethnic groups remains essential.Methods: Our study aimed to address this need by conducting integrated in silico analyses to identify actionable genomic alterations in gastric cancer driver genes, assess their impact using deleteriousness scores, and determine allele frequencies across nine global populations: European Finnish, European non-Finnish, Latino, East Asian, South Asian, African, Middle Eastern, Ashkenazi Jewish, and Amish. Furthermore, our goal was to prioritize targeted therapeutic strategies based on pharmacogenomics clinical guidelines, in silico drug prescriptions, and clinical trial data.Results: Our comprehensive analysis examined 275,634 variants within 60 gastric cancer driver genes from 730,947 exome sequences and 76,215 whole-genome sequences from unrelated individuals, identifying 13,542 annotated and predicted oncogenic variants. We prioritized the most prevalent and deleterious oncogenic variants for subsequent pharmacogenomics testing. Additionally, we discovered actionable genomic alterations in the ARID1A, ATM, BCOR, ERBB2, ERBB3, CDKN2A, KIT, PIK3CA, PTEN, NTRK3, TP53, and CDKN2A genes that could enhance the efficacy of anti-cancer therapies, as suggested by in silico drug prescription analyses, reviews of current pharmacogenomics clinical guidelines, and evaluations of phase III and IV clinical trials targeting gastric cancer driver proteins.Discussion: These findings underline the urgency of consolidating efforts to devise effective prevention measures, invest in genomic profiling for underrepresented populations, and ensure the inclusion of ethnic minorities in future clinical trials and cancer research in developed countries.

Interventions to Reduce Child Maltreatment: A Systematic Review with a Narrative Synthesis

AbstractChild maltreatment has been a prominent topic on the political agenda for the past decade. However, while there are several types of interventions that can potentially benefit the prevention of child maltreatment, uncertainties remain regarding the transferability of these interventions to different contexts and their overall impact. Consequently, we conducted a systematic review of intervention studies aimed at preventing child maltreatment. We searched for studies published between 2016 and 2021, using predefined keywords from various bibliographical databases including PsycINFO, SocINDEX, Social Care Online, Web of Science, and ASSIA. The initial literature search yielded 3221 studies based on titles and abstracts, after removing duplicates. Out of these, 251 studies were screened based on full texts, resulting in the selection of 56 studies that met our inclusion criteria and were retained for extraction and analysis. The screening and data extraction processes were conducted by at least two independent reviewers. Given the heterogeneity of the included studies, we performed a narrative synthesis and categorized the 56 studies based on intervention type, control condition, outcomes, effects and quality. The results indicated that most of the studies employed individual randomization, with the control group most often receiving treatment as usual. Home visiting programs and educational interventions emerged as the most prevalent types of interventions. The review also demonstrated that a significant number of the included studies reported positive effects on one or more outcomes, such as indicators of maltreatment, suboptimal parenting practices, and problematic child behaviors. While nearly one-third of the studies did not report an effect size, those that did reported varying types of effect sizes. Additionally, only a few studies met the assessed quality criteria.