Cluster Groups Research Articles

A novel prognostic risk-scoring system based on m5C methylation regulator-mediated patterns for glioma patients

N5-methylcytosine (m5C) methylation modification plays a crucial role in the epigenetic mechanisms underlying tumorigenesis, aggressiveness, and malignancy in diffuse glioma. Our study aimed to develop a novel prognostic risk-scoring system to assess the impact of m5C modification in glioma patients. Initially, we identified two distinct m5C clusters based on the expression level of m5C regulators in The Cancer Genome Atlas glioblastoma (TCGA-GBM) dataset. Differentially expressed genes (DEGs) between the two m5C cluster groups were determined. Utilizing these m5C regulation-related DEGs, we classified glioma patients into three gene cluster groups: A, B, and C. Subsequently, an m5C scoring system was developed through a univariate Cox regression model, quantifying the m5C modification patterns utilizing six DEGs associated with disease prognosis. The resulting scoring system allowed us to categorize patients into high- or low-risk groups based on their m5C scores. In test (TCGA-GBM) and validation (Chinese Glioma Genome Atlas [CGGA]-1018 and CGGA-301) datasets, glioma patients with a higher m5C score consistently exhibited shorter survival durations, fewer isocitrate dehydrogenase (IDH) mutations, less 1p/19q codeletion and higher World Health Organization (WHO) grades. Additionally, distinct immune cell infiltration characteristics were observed among different m5C cluster groups and risk groups. Our study developed a novel prognostic scoring system based on m5C modification patterns for glioma patients, complementing existing molecular classifications and providing valuable insights into prognosis for glioma patients.

Development of a nuanced approach to aggressive driving: An investigation into the self-regulatory and attitudinal profiles of aggressive drivers and their contextual role in negative road interactions

This study aimed to provide a novel perspective on aggressive driving behaviour (ADB) by developing a self-report measure of aggressive driving styles (the Aggressive Driving Styles Questionnaire; ADSQ) and comparing how different types of drivers (according to their attitudes and self-regulatory functioning) might differentially engage in such driving styles. An online survey was completed by a sample of Australian adult drivers (N = 527), who reported engaging in an ADB in the past month. An initial confirmatory factor analysis indicated that the proposed factor structure of instrumental, reactive, proactive and interpersonal driver aggression, was a good fit for the data, and reliability tests suggested that the ADSQ had good internal consistency (α = 0.73 - 0.91). Next, correlational analysis showed that the ADSQ had positive associations with (a) driver anger and rumination; (b) problematic attitudes towards instrumental and hostile aggressive driving; and (c) various domains (i.e., cognitive, attentional, emotional) of self-regulatory dysfunctioning. K-means cluster analysis was then used on the variables to determine potential profiles of ‘self-regulated’ drivers among the participants. The results suggested that three driver types significantly differed according to their attitudes and level of self-regulatory dysfunction: self-regulated drivers (high functioning; low attitudes), antisocial drivers (medium functioning; high attitudes), and dysregulated drivers (low functioning, high attitudes). These cluster groups were shown to significantly differ in their tendency to engage in aggressive driving styles and what behaviours they perceived as most triggering when driving on the road. The ADSQ and associated findings of this study provide a nuanced perspective on how ADBs can be approached, observed, and operationalised.

Assessing Oral Health in U.S. Children and Its Impact on Disparities of Overall Health and Health Expenditures: An Application Using Two-Stage Clustering

We assess children’s oral and overall health using an innovative clustering methodology. Prior studies have documented the link between oral health and overall health, such as dental caries, which could lead to infections and pain, or periodontal disease, which is associated with other conditions, such as diabetes, heart disease, cardiovascular disease, and adverse pregnancy outcomes. Using the nationally representative Medical Expenditure Panel Study data, we link children’s dental care services and other control variables with various health outcomes using a two-stage clustering method. In the first stage, we cluster similar children using their social determinants of health, medical conditions, and dental care utilization as input variables. In the second stage, we identify groups of similar clusters using a metric called cosine similarity. External variables, rather than those used to create the clusters, are used to assess the effectiveness of the clusters. An outcome score is created based on children’s favorable overall and mental health statuses and absence of behavioral issues at home, school, and in general. We find that the first-stage clusters are separated into three main groups in the second stage, where one cluster from the first stage is very different from all of the other clusters, a second group has more favorable outcome scores, and the third group has lower outcome scores. Lower dental expenditures are associated with lower outcome scores. Clusters are useful for cost management purposes to identify subgroups for interventions to improve population health. Comparisons of access and outcomes of children between different insurance types can provide beneficial information for insurance design and public policy. Our approach can be applied to other areas of practice where there is an interest in grouping similar individuals and relating these groups to other quantities. The two-stage clustering method is a versatile method with a broader appeal to empirical research, especially when there are big data, a large number of clusters, and many input variables.

Methodology for calculating load capacity reduction and slip relationship of clustered group nail connectors

Clustered group nail connectors are key connecting components for the full lifecycle construction and safe operation of steel–concrete composite structural bridges. To thoroughly investigate the stress mechanism of clustered group nail connectors in steel–concrete composite structures, this paper conducts a detailed numerical analysis on 100 sets of such connectors. It analyzes the stress mechanism of individual nail connectors and quantitatively calculates the group nail effect under the coupled action of multiple factors (nail spacing between layers, number of nail layers, concrete strength). Based on clarifying the force transmission patterns of nails and concrete in different layers during the loading process, this paper proposes a method for calculating the average bearing capacity reduction coefficient and the load-slip curve of single nails in clustered group nail connectors under the coupled action of multiple factors, which has been validated by experimental data. This research provides a theoretical basis for the design and calculation of group nail connectors in steel–concrete composite structural bridges.

A Data Mining Study for Analysis of Acupoint Selection and Combinations in Acupuncture Treatment of Carpal Tunnel Syndrome.

Carpal tunnel syndrome (CTS) is the most prevalent upper limb compressive neuropathy. A considerable number of clinical trials and meta-analyses have provided evidence supporting the effectiveness of acupuncture in treating CTS. Nevertheless, the ideal choice of acupoints remains ambiguous. A data mining analysis was conducted with the objective of determining the most effective acupoint combinations and selection for CTS. A search was conducted across seven Chinese and English electronic bibliographic databases spanning from their inception to March 2023. Selected were clinical trials that evaluated the efficacy of acupuncture therapy for CTS, with or without randomised controlled methods. Data extraction mainly included acupoint prescriptions. Information such as first author, study design and study setting were also extracted. The principal outcomes comprised the clinical manifestations linked to CTS. Statistical descriptions were generated using Excel 2019. The analysis of association rules was conducted using SPSS Modeler 18.0. Using SPSS Statistics 26.0, exploratory factor analysis and cluster analysis were conducted. 142 trials (including 86 RCTs and 56 non RCTs) were identified, and 193 groups of effective prescriptions involving 68 acupoints were extracted. The most frequently used acupoints were Da-ling (PC7), Nei-guan (PC6), He-gu (LI4), Wai-guan (TE5), and Yang-xi (LI5). The most frequently used meridians were the pericardial meridian and the large intestine meridian. The majority of special acupoints used were Five-shu points and Yuan-source points, with acupoints on the upper limbs being the most frequently used. The core acupoint groups were analyzed and 11 groups of association rules, 8 factors, and 5 effective cluster groups were obtained. The evidence-based acupoint selection and combinations of acupuncture therapy for carpal tunnel syndrome were provided by the findings of this study.

K-MEANS CLUSTER ANALYSIS RELATED TO UNMET NEED FOR FAMILY PLANNING IN BANYUWANGI, INDONESIA: A CASE STUDY

Background: The population growth rate in Indonesia from 2010-2020 was 1.25% per year. The rate of population growth must be accompanied by an increase in the quality of human life. Human quality of life begins from within the womb, so that preventive efforts can be undertaken. The Family Planning Program was implemented to overcome the problem of population density so that it becomes more controlled. However, in line with the existence of the family planning program, there are still incidents of unmet need for family planning that occur among couples of productive ages. Purpose: This study aims to undertake a cluster analysis to see which variables are the dominant reasons for couples of childbearing ages to have unmet needs. Methods: This research was conducted using the K-Means cluster analysis method, using secondary data in 25 sub-districts from the Banyuwangi Regency Social, Women's Empowerment and Family Planning Service. Results: Research showed that 3 clusters were formed, each cluster had a dominant incidence of unmet need. Cluster 1 was dominant in Drop Out incidents in 14 sub-districts, Cluster 2 was dominant in IAT incidents in 9 sub-districts, and Cluster 3 was dominant in TIAL incidents in 2 sub-districts. Conclusion: The implementation of cluster grouping can make it easier for officers to focus on reducing the number of unmet need incidents that occur among residents in each sub-district.

Abstract B015: A novel Cluster 3 Endometrial Cancer PDX model to test hormone therapy regimens

Abstract Endometrial cancer is the most commonly diagnosed gynecologic cancer and continues to rise in incidence and mortality rates worldwide. With the evaluation of large datasets including the TCGA, endometrial cancers have been subdivided into four different cluster groups based on molecular characteristics. The most common subtype of endometrial cancer is the Cluster 3 category, demarcated by the expression of estrogen receptors (ER) and progesterone receptors (PR) with the potential to respond to hormonal therapy. However, Cluster 3 tumors frequently lose hormone receptor expression over time, particularly PR, making them resistant to treatment. Although numerous cell line models exist to study this disease, there is a need for improved models to select best treatment options for the specific types of endometrial cancers. Here, we describe the development of an endometrial cancer patient derived xenograft (PDX), EC-PDX3. The initial tumor from which EC-PDX3 was derived was a low-grade endometrioid endometrial carcinoma that contained a low number of somatic copy number alterations. The tumor initially tested robustly positive for both ER and PR in the donor patient as well as in initial passages in athymic mice, making this model valuable for hormone therapy research. In these studies, EC-PDX3 cells were grown within immunodeficient NSG mice. We performed H&E, ER and PR staining prior to experimentation and found that while ER remained highly positive, PR levels were significantly downregulated, indicating the development of a hormone resistant state similar to that occurring in patients over time. To determine if these cells respond to hormonal therapy and if PR expression could be induced, mice were initially treated with estradiol to enhance PR expression. Next, mice were further treated with progesterone or onapristone, an experimental progestin agonist/antagonist with previously reported encouraging activity in advanced endometrial cancer in phase I and II trials. Tumors were measured weekly to determine hormone effects on cancer growth. Mice treated with estradiol alone showed substantially larger tumor growth as compared to the estradiol + progesterone and estradiol + onapristone treatment groups, indicating that hormone sensitivity was likely re-instated. RNA sequencing using Ishikawa cells in culture revealed the transcriptional effects of estradiol, progesterone and onapristone which complement the data obtained from EC-PDX3 bearing mice treated similarly. These studies identify interesting growth inhibitory effects of onapristone in endometrial cancer cells primed with estradiol including the significant induction of the cell cycle regulators such as p21 and genes involved in apoptosis. In addition, EC-PDX3 provides a model to assess hormonal therapy in Cluster 3 tumors that initially respond but develop resistance to treatment over time. Citation Format: Kimberly K. Leslie, Lane E. Smith, Jamie Padilla, Adrian Quintana. A novel Cluster 3 Endometrial Cancer PDX model to test hormone therapy regimens [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr B015.

Implementation of an enhanced recovery after surgery protocol for colorectal cancer in a regional hospital network supported by audit and feedback: a stepped wedge, cluster randomised trial

BackgroundEnhanced recovery after surgery (ERAS) protocols are known to potentially improve the management and outcomes of patients undergoing colorectal surgery, with limited evidence of their implementation in hospital networks and...

Penerapan Data Mining Dengan Menggunakan Algoritma Clustering K-Means Untuk Pembagian Jurusan Pada Sekolah Menengah Atas

Senior High School is the last level that must be taken before continuing education at a higher level such as a Diploma or Bachelor's degree. Where in general high schools have class majors for students who will move up to class XI from class Improving the quality of education carried out in the class majoring process means that students will be more focused in accordance with the field of interest of the major that the student/I should take. The process that occurs in determining majors is only based on the wishes expressed by the students without taking into account the academic grades of the subjects that the students have passed or completed in class X. This problem is not a small problem that should be ignored, it This is an important problem that must be resolved immediately because if the problem is not resolved immediately it will have lasting impacts later. The process of determining the division of majors for students can be seen based on the patterns or values of previous students. Data mining is a process used to complete processing of large data. The data that is processed is a collection of data that becomes Big Data from past data that is stored in a storage container and can then be reused by processing it. Clustering is an appropriate way to solve problems. Where in clustering grouping is carried out based on the distance to each data object. The K-Means algorithm is part of Clustering Data Mining, where this algorithm can be used to carry out new groupings based on how clusters are formed. From the results obtained, there are 2 (two) new formation clusters. In cluster 1 there are 9 (nine) students and in cluster 2 there are 6 (six) students.

An Enhanced Energy Efficiency Routing for WSN based on Elephant Herding and Swarm Optimization Approaches

Energy utilization and inadequacy of sensor nodes are considered major drawbacks in wireless sensor networks (WSNs). This is because the sensor nodes use the battery for recharging energy. To overcome this issue WSN utilized a clustering-routing algorithm. This protocol divides the adjacent sensor nodes into separate clusters to choose a cluster head. Thus, the cluster head gathers information from all clusters and transmits it to the base station. In this article, the proposed method used cluster-based routing protocols to enhance energy efficiency and network lifetime. Moreover, this paper follows three stages to maximize energy efficiency. Initially, the clustering process is performed using dolphin swarm optimization (DSO), where a group of clusters is formed. Then the second stage is composed of cluster head selection among the group of clusters by elephant herding optimization (EHO) strategy. Finally, the collected data are necessary to forward to the base station for transferring the information. A specified path (routing) is selected by chicken swarm optimization (CSO). By using these algorithms, the network nodes support the balance of energy utilization. Experimental analysis proves when evaluated with existing methods the proposed technique has improved energy efficiency with an increase in network lifetime.

Acupoint selection rules of acupuncture and moxibustion in treating neurogenic bladder based on data mining.

To explore the rules of acupoint selection in the treatment of neurogenic bladder (NB) with acupuncture and moxibustion by using data mining. The clinical research literatures on acupuncture treatment of NB were collected from PubMed, Embase, Cochrane Library, CNKI, Wanfang Database, VIP Database and China Biology Medicine from retrieved to January 1, 2023. The acupoint prescription database was established using Excel 2019. SPSS Modeler 18.0 and SPSS Statistics 26.0 softwares were used to conduct the frequency, meri-dians, locations, specific acupoints analysis and association rules analysis, factor analysis, cluster analysis, etc., to explore the characteristics and rules of acupoint selection in acupuncture and moxibustion treatment of NB. Totally 313 papers were included, including 110 acupoints with a total frequency of 1 995. The high-frequency acupoints are Zhongji (CV3), Guanyuan (CV4), Sanyinjiao (SP6), etc. The commonly used meridians are the Bladder Meridian of Foot Taiyang and Conception Vessel. The involved acupoints are mostly located in the lumbosacral region and abdomen, and intersection acupoints, mu-front acupoints and back-shu acupoints are the majority in the specific acupoints. The core acupoints group was analyzed, and 17 groups of association rules, 7 factors and 6 effective cluster groups were obtained. Acupuncture and moxibustion treatment of NB follows the therapeutic principles of toni-fying the kidney, invigorating the spleen, and soothing the liver. The core acupoints group is CV3-CV4-SP6.

Comparison of K-Means and Fuzzy C-Means Algorithms for Clustering Based on Happiness Index Components Across Provinces in Indonesia

Cluster analysis is a multivariate technique aimed at grouping objects into several clusters based on the characteristics they possess. This study aims to determine the clustering results of 34 provinces in Indonesia based on the indicators of the happiness index for the year 2021 by comparing non-hierarchical cluster analysis methods, namely K-Means and Fuzzy C-Means. K-Means is a non-hierarchical cluster analysis that divides objects into cluster groups based on the distance of objects to the nearest cluster center, while Fuzzy C-Means is a cluster analysis that uses a fuzzy grouping model where data becomes a member of a cluster formed based on membership degrees ranging from 0 to 1. Based on the research results, it is known that clustering with both K-Means and Fuzzy C-Means methods forms three clusters. Based on the standard deviation values between groups and the standard deviation ratio, the best method is the Fuzzy C-Means method because it has a larger standard deviation between groups and a smaller ratio compared to the K-Means method, which is 0.6680004. Therefore, this study concludes that the Fuzzy C-Means method is more optimal than the K-Means method.

Effectiveness of pyriproxyfen-pyrethroid and chlorfenapyr-pyrethroid long-lasting insecticidal nets (LLINs) compared with pyrethroid-only LLINs for malaria control in the third year post-distribution: a secondary analysis of a cluster-randomised controlled trial in Benin

Malaria continues to kill approximately 650 000 people each year. There is evidence that some second-generation insecticide-treated nets, which combine insecticide formulations with different modes of action, are protective against malaria while the nets are new; however, evidence for their impact over 3 years is scarce. In this study, we report the third-year results of a cluster-randomised controlled trial assessing the long-term effectiveness of dual-active ingredient long-lasting insecticidal nets (LLINs). This is a secondary analysis of a cluster-randomised controlled trial, carried out between May 23, 2019, and April 30, 2023, in southern Benin. Restricted randomisation was used to assign 60 clusters (villages or groups of villages with a minimum of 100 households) to the three study groups (1:1:1) to evaluate the efficacy of pyriproxyfen-pyrethroid LLINs and chlorfenapyr-pyrethroid LLINs compared with pyrethroid-only LLINs (reference) against malaria transmission. The study staff and communities were masked to the group allocation. The primary outcome was malaria incidence measured over the third year after LLIN distribution, in a cohort of children aged 6 months to 9 years at the time of enrolment, in the intention-to-treat population. Here, we present the data of the third year post-LLIN distribution. The trial was registered with ClinicalTrials.gov, NCT03931473. Study net use declined over the 3 years and was consistently lowest in the pyriproxyfen-pyrethroid LLIN group (at 36 months: 889 [39·4%] of 2257 participants vs 1278 [52·2%] of 2450 participants for the chlorfenapyr-pyrethroid LLIN group and 1400 [57·6%] of 2430 participants for the pyrethroid-only LLIN group). The cohort of children for the third year of follow-up (600 per group) were enrolled between April 9 and 30, 2022. Mean malaria incidence during the third year after distribution was 1·19 cases per child-year (95% CI 1·09-1·29) in the pyrethroid-only LLIN reference group, 1·21 cases per child-year (1·12-1·31) in the pyriproxyfen-pyrethroid LLIN group (hazard ratio [HR] 1·02, 95% CI 0·71-1·44; p=0·92), and 0·96 cases per child-year (0·88-1·05) in the chlorfenapyr-pyrethroid LLIN group (HR 0·80, 0·56-1·17; p=0·25). No adverse events related to study nets were reported by participants. During the third year, as was also observed during the first 2 years, the pyriproxyfen-pyrethroid LLIN group did not have superior protection against malaria cases compared with the standard LLIN group. In the third year, people living in the chlorfenapyr-pyrethroid LLIN group no longer benefited from greater protection against malaria cases and infections than those living in the pyrethroid-only LLIN group. This was probably influenced by lower study net use than previous years and the declining concentration of partner insecticides in the nets. UNITAID, The Global Fund. For the French translation of the abstract see Supplementary Materials section.

Screening cotton genotypes for their drought tolerance ability based on the expression level of dehydration-responsive element-binding protein and proline biosynthesis-related genes and morpho-physio-biochemical responses.

Drought stress adversely affects growth, development, productivity, and fiber quality of cotton (Gossypium hirsutum L). Breeding strategies to enhance drought tolerance require an improved knowledge of plant drought responses necessitating proper identification of drought-tolerant genotypes of crops, including cotton. The objective of this study was to classify the selected cotton genotypes for their drought tolerance ability based on morpho-physio-biochemical traits using Hierarchical Ward's cluster analysis. Five genotypes of cotton (Takfa 3, Takfa 6, Takfa 7, Takfa 84-4, and Takfa 86-5) were selected as plant materials, and were grown under well-watered (WW; 98 ± 2% field capacity) and water-deficit (WD; 50 ± 2% field capacity) conditions for 16days during the flower initiation stage. Data on morpho-physio-biochemical parameters and gene expression levels for these parameters were collected, and subsequently genotypes were classified either as a drought tolerant or drought susceptible one. Upregulation of GhPRP (proline-rich protein), GhP5CS (Δ1-pyrroline-5-carboxylate synthetase), and GhP5CR (Δ1-pyrroline-5-carboxylate reductase) in relation to free proline enrichment was observed in Takfa 3 genotype under WD condition. An accumulation of free proline, total soluble sugar, and potassium in plants under WD conditions was detected, which played a key role as major osmolytes controlling cellular osmotic potential. Magnesium and calcium concentrations were also enriched in leaves under WD conditions, functioning as essential elements and regulating photosynthetic abilities. Leaf greenness, net photosynthetic rate, stomatal conductance, and transpiration rate were also declined under WD conditions, leading to growth retardation, especially aboveground traits of Takfa 6, Takfa 7, Takfa 84-4, and Takfa 86-5 genotypes. An increase in leaf temperature (1.1 - 4.0°C) and crop water stress index (CWSI > 0.75) in relation to stomatal closure and reduced transpiration rate was recorded in cotton genotypes under WD conditions compared with WW conditions. Based on the increase of free proline, soluble sugar, leaf temperature, and CWSI, as well as the decrease of aboveground growth traits and physiological attributes, five genotypes were categorized into two cluster groups: drought tolerant (Takfa 3) and drought susceptible (Takfa 6, Takfa 7, Takfa 84-4, and Takfa 86-5). The identified drought-tolerant cotton genotype, namely, Takfa 3, may be grown in areas experiencing drought conditions. It is recommended to further validate the yield traits of Takfa 3 under rainfed field conditions in drought-prone environments.

A cluster analysis of reasons behind fear of birth among women in Sweden.

Fear of birth is common and complex, caused by a variety of reasons. The aim was to investigate the prevalence of pre-established reasons in relation to fear, and to identify profiles of women based on their reported reasons behind fear of birth. A cross-sectional Swedish study of women with self-reported fear of birth who completed an online survey. Descriptive statistics, chi-square test, crude and adjusted odds ratios with 95% confidence intervals were used in the analysis of pre-established reasons in relation to self-reported severe fear. A Kappa-means cluster analysis was performed in order to group reasons, that were further investigated in relation to women's background variables. A total of 1419 women completed the survey. The strongest reason behind fear of birth was to be forced to give birth vaginally. Four clusters were identified and labeled: minor complexity (reference group), relative minor complexity, relative major complexity, and major complexity. Cesarean section preference, previous mental health problems, being younger, primiparity, and exposure to domestic violence were factors related to cluster grouping. Women with fear of birth have various reasons and diverse complexities behind their fear. Health care providers need to investigate these reasons and support pregnant women with childbirth fear, based on their needs.

Exploratory analyses of homologous recombination repair (HRR) gene subgroups and potential associations with secondary efficacy endpoints in the HRR-deficient population from TALAPRO-2.

178 Background: TALAPRO-2 demonstrated statistically significant improvement in radiographic PFS with 1L talazoparib (TALA) + enzalutamide (ENZA) in patients (pts) with metastatic castration-resistant prostate cancer (mCRPC) with and without HRR gene alterations (HRRm). Tumors were prospectively determined to harbor HRRm. Pts were randomized 1:1 to TALA 0.5 mg (N=200) or placebo (PBO; N=199) + ENZA 160 mg QD. Exploratory biomarker analyses assessed HRR gene subgroups and potential associations with secondary efficacy endpoints in pts with HRRm tumors. Methods: Prospective testing used a 12-gene HRR panel (HRR12) of FoundationOne CDx/FoundationOne Liquid CDx.For single gene groups, only pts bearing alteration(s) in that gene and no other HRR12 genes were analyzed. Gene cluster alteration dominance hierarchy: any BRCA1/2 alteration ( BRCA cluster); any PALB2 ( PALB2 cluster); any CDK12 ( CDK12 cluster); any ATM ( ATM cluster); all other HRR12 genes (each pt counted once). Endpoints: ORR, time to progression or death on first subsequent antineoplastic therapy (PFS2), PSA response ≥50%, time to PSA progression (TTPP), and time to initiation of cytotoxic chemotherapy (TTCC). Results: TALA + ENZA demonstrated differential benefit for BRCA2 single gene group across endpoints: ORR, 78.9% vs 33.3% (odds ratio [OR], 0.13 [95% CI, 0.03–0.63]); PFS2, median not reached (NR) vs 26.1 mo (HR, 0.44 [0.20–0.97]); PSA response >50%, 87.0% vs 58.3%; TTPP, NR vs 9.2 mo (HR, 0.20 [0.10–0.41]); TTCC, NR vs 17.0 mo (HR, 0.27 [0.13–0.57]). Similar broad benefit was seen for BRCA cluster and BRCA1 single gene groups and to a lesser extent PALB2 single gene and cluster groups (allowing for small n in the latter 3 groupings). Broad differential benefit was evident for CDK12 single gene group : ORR 70.0% vs 28.6% (OR 0.17 [0.01–2.03]); PFS2, 36.4 vs 18.8 mo (HR, 0.29 [0.11–0.75]); PSA response ≥50%, 85.7% vs 53.3%; TTPP, 13.8 vs 11.1 mo (HR 0.61 [0.29–1.26]); the exception was TTCC, NR vs NR (HR 1.32 [0.43–4.06]). Similar benefit was seen for CDK12 cluster. For ATM single gene group, differential numerical benefit was seen for ORR, 81.8% vs 20.0% (OR 0.06 [0.00–1.17]); TTPP, 26.8 vs 15.9 mo (HR, 0.61 [0.25–1.47]); TTCC, NR vs NR (HR 0.60 [0.18–1.96]); no differential benefit was seen in other endpoints (similar for ATM cluster). The CHEK2 single gene group showed numerically differential benefit for PSA endpoints and TTCC. The other HRR12 gene cluster typically showed comparable efficacy between arms with potential exception of TTPP, 28.6 vs 14.0 mo (HR 0.65 [0.33–1.31]), and TTCC, NR vs NR (HR 0.65 [0.26–1.62]). Conclusions: Broad differential efficacy benefit was evident for TALA + ENZA vs PBO + ENZA across multiple molecular subgroups and was most pronounced for the BRCA1- PALB2- BRCA2 axis and CDK12. Additional analyses are warranted. Clinical trial information: NCT03395197 .

Abstract A059: Heterogeneous cell populations in organoid models derived from breast preinvasive and invasive carcinomas after endocrine therapy

Abstract Approximately 80% of ductal carcinoma in situ (DCIS) and invasive breast cancers express hormone receptors (HR), and are typically treated with selective estrogen receptor modulators (SERMs) or aromatase inhibitors, or with additional HR-targeting agents in the research setting. However, the development of resistance poses a significant challenge. To better understand the mechanisms behind endocrine resistance and explore new treatment strategies, it is essential to have a reliable model system that accurately reflects the heterogeneous nature of breast lesions. In this study, we developed a protocol to generate patient-derived organoid cultures of pre-invasive and malignant breast lesions from patients with persistent or residual disease in the active surveillance or neoadjuvant setting after treatment with endocrine therapy. We generated n = 11 HR+ DCIS and n = 18 HR+ breast cancer organoids and used single-cell RNA-sequencing to characterize cell heterogeneity within individual organoid cultures. We observed mixed non-cancerous and malignant cells present within some cultures, and optimized protocols to minimize non-cancerous cell types. To better define endocrine sensitivity versus resistance, we also assessed responsiveness to estrogen and anti-estrogen therapy in individual cultures by monitoring organoid growth through live cell imaging and evaluating estrogen-response markers at the protein and RNA levels in response to treatment in vitro. Next, we applied our model system to evaluate endocrine resistance in heterogeneous tumor populations. We compared changes in cellular composition and signaling pathways in endocrine-resistant and endocrine-sensitive breast organoids based on in vitro metrics and/or correlation with clinical variables. In addition, within single cultures heterogeneous cell populations were identified with differential expression of ESR1 and HR target genes, generally falling into three cluster groups that could be defined based on different patterns of FOXA1, GATA3, TFF1, and XBP1 expression, among others. These populations also differed with evidence of co-expressed genes and signaling pathways related to breast tumorigenesis and differentiation (estrogen response, androgen response, mTOR signaling, metabolism) in putative HR-active cell populations in both pre-invasive and invasive breast cases. In summary, our study demonstrates that patient-derived breast and breast cancer organoids can be used to model estrogen responsiveness and endocrine resistance. We have established a platform that enables us to investigate the mechanisms underlying endocrine resistance at different premalignant and tumor stages to evaluate new treatment approaches. The ultimate goal of this work is to contribute to the development of new targeted therapies for endocrine-resistant breast lesions. Citation Format: Nadine Goldhammer, Shruti Warhadpande, Liana Beld, Cristian K. Maldonado Rodas, Allison Lam, Tam Binh Bui, Shoko Emily Abe, Michael Alvarado, Cheryl Ewing, Karen Goodwin, Rita Mukhtar, Jasmine Wong, Lamorna Brown Swigart, Gillian Hirst, Christopher Schwartz, Laura van't Veer, Laura Esserman, Jo Chien, Jennifer M. Rosenbluth. Heterogeneous cell populations in organoid models derived from breast preinvasive and invasive carcinomas after endocrine therapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A059.

Research on load curve fitting method based on K-means algorithm

Some areas of China are affected by the fluctuation of new energy output and load, and the power supply as a whole presents an unbalanced phenomenon of abundant electricity supply but shortage of power supply. To understand the characteristics of the load electricity consumption curve more accurately, this paper proposes a method of fitting the load electricity consumption curve based on K-mea ns algorithm. First, analyze the characteristics of the load electricity consumption based on the industry subdivision, and build the load electricity consumption characteristic index system. Then, the k-mea ns cluster analysis is carried out based on the characteristics of the load power consumption curve of the subdivided industries to determine the number of clusters, and the curve fitting and deviation test are carried out for each cluster group. This paper carries out practical analysis and theoretical research based on the data of typical industry load power consumption curve in Ningxia.

Single-cell RNA-seq mapping of chicken peripheral blood leukocytes

BackgroundSingle-cell transcriptomics provides means to study cell populations at the level of individual cells. In leukocyte biology this approach could potentially aid the identification of subpopulations and functions without the need to develop species-specific reagents. The present study aimed to evaluate single-cell RNA-seq as a tool for identification of chicken peripheral blood leukocytes. For this purpose, purified and thrombocyte depleted leukocytes from 4 clinically healthy hens were subjected to single-cell 3′ RNA-seq. Bioinformatic analysis of data comprised unsupervised clustering of the cells, and annotation of clusters based on expression profiles. Immunofluorescence phenotyping of the cell preparations used was also performed.ResultsComputational analysis identified 31 initial cell clusters and based on expression of defined marker genes 28 cluster were identified as comprising mainly B-cells, T-cells, monocytes, thrombocytes and red blood cells. Of the remaining clusters, two were putatively identified as basophils and eosinophils, and one as proliferating cells of mixed origin. In depth analysis on gene expression profiles within and between the initial cell clusters allowed further identification of cell identity and possible functions for some of them. For example, analysis of the group of monocyte clusters revealed subclusters comprising heterophils, as well as putative monocyte subtypes. Also, novel aspects of TCRγ/δ + T-cell subpopulations could be inferred such as evidence of at least two subtypes based on e.g., different expression of transcription factors MAF, SOX13 and GATA3. Moreover, a novel subpopulation of chicken peripheral B-cells with high SOX5 expression was identified. An overall good correlation between mRNA and cell surface phenotypic cell identification was shown.ConclusionsTaken together, we were able to identify and infer functional aspects of both previously well known as well as novel chicken leukocyte populations although some cell types. e.g., T-cell subtypes, proved more challenging to decipher. Although this methodology to some extent is limited by incomplete annotation of the chicken genome, it definitively has benefits in chicken immunology by expanding the options to distinguish identity and functions of immune cells also without access to species specific reagents.

Infants on the move: bibliometric analyses of observational vs. digital means of screening infant development.

Neurodevelopmental disorders are on the rise, yet their average diagnosis is after 4.5 years old. This delay is partly due to reliance on social-communication criteria, which require longer maturation than scaffolding elements of neuromotor control. Much earlier criteria could include reflexes, monitoring of the quality of spontaneous movements from central pattern generators and maturation of intentional movements and their overall sensation. General Movement Assessment (GMA) studies these features using observational means, but the last two decades have seen a surge in digital tools that enable non-invasive, continuous tracking of infants' spontaneous movements. Despite their importance, these tools are not yet broadly used. In this work, using CiteSpace, VOSViewer and SciMAT software, we investigate the evolution of the literature on GMA and the methods in use today, to estimate how digital techniques are being adopted. To that end, we created maps of key word co-occurrence networks, co-author networks, document co-citation analysis and strategic diagrams of 295 publications based on a search in the Web of Science, Dimensions and SCOPUS databases for: 'general movement assessment' OR 'general movements assessment'. The nodes on the maps were categorized by size, cluster groups and year of publication. We found that the state-of-the-art methodology to diagnose neurodevelopmental disorders still relies heavily on observation. Several groups in classical GMA research have branched out to incorporate new techniques, but few groups have adopted digital means. We report on additional analyses of methods and biosensors usage and propose that combining traditional clinical observation criteria with digital means may allow earlier diagnoses and interventional therapies for infants.