Single Regression Research Articles

Evaluation of shaft angle to ball-to-target line as a predictor of horizontal delivery plane angle in the golf swing

ABSTRACT The shaft angle to the ball-to-target line at various points in the golf swing is used by coaches as an indication of the horizontal delivery plane angle (HPA). The aim of the current study was to understand to what degree this simplified method of using the shaft orientation can predict the orientation of the HPA. Fifty-two male golfers hit 40 drives each in an indoor biomechanics laboratory. Between-subject regression models were created for the relationship between the HPA and the shaft angle to the ball-to-target line at three different swing positions. Additionally, single subject regression models were created for each subject for the small variables. The only significant between-subjects regression model was for mid-downswing (Adjusted R2 = 89.5%, RMSE = 2.41°); however, this was deemed not accurate enough to distinguish differences between typical driver and wedge HPA. The only shaft position to have significant single-subject regression models for all participants was mid-downswing. The mean RMSE for those models was determined to be low enough to distinguish typical driver and wedge swing planes within individuals. Overall, the shaft angle was only deemed accurate enough to predict the HPA within individual subjects, and only for mid-downswing.

Study of the RNA splicing kinetics via in vivo 5-EU labeling.

Splicing is an important step of gene expression in all eukaryotes. Splice sites might be used with different efficiency, giving rise to alternative splicing products. At the same time, splice sites might be used at a variable rate. We used 5-ethynyl uridine labeling to sequence a nascent transcriptome of HeLa cells and deduced the rate of splicing for each donor and acceptor splice site. The following correlation analysis showed a correspondence of primary transcript features with the rate of splicing. Some dependencies we revealed were anticipated, such as a splicing rate decrease with a decreased complementarity of the donor splice site to U1 and acceptor sites to U2 snRNAs. Other dependencies were more surprising, like a negative influence of a distance to the 5' end on the rate of the acceptor splicing site utilization, or the differences in splicing rate between long, short, and RBM17-dependent introns. We also observed a deceleration of last intron splicing with an increase of the distance to the poly(A) site, which might be explained by the cooperativity of the splicing and polyadenylation. Additional analysis of splicing kinetics of SF3B4 knockdown cells suggested the impairment of a U2 snRNA recognition step. As a result, we deconvoluted the effects of several examined features on the splicing rate into a single regression model. The data obtained here are useful for further studies in the field, as they provide general splicing rate dependencies as well as help to justify the existence of slowly removed splice sites.

Sexual dimorphism of pelvic scarring: A new method of adult biological sex estimation.

Estimating biological sex is a crucial aspect of forensic anthropology, and is pivotal in forensic investigations. Presently, the most frequently adopted osteological sex estimation methods focus on the anterior pelvis, which is easily susceptible to postmortem damage, revealing a need for additional accurate methods. This study introduces a novel method for estimating adult sex through metric pelvic scar analysis, using a known skeletal sample (169 females; 51 males). Relationships between sex and scar dimensions were subjected to Kendall's tau-B testing, and the strongest associated measurements were further analyzed using binary logistic regression to determine their predictive capacity. The final estimation method was tested on an additional known-sex sample of 43 males and 43 females from the Spitalfields skeletal collection. All associations between biological sex and scar measurements were significant, with the preauricular sulcus and newly defined inferior interosseous cavity presenting the strongest relationships (τb 0.223-0.504). Individual regression models using the approximate volume of each feature predicted sex with over 80% accuracy, but when combined in a single regression model, the accuracy increased to an impressive 97.1%. When then applied to the validation sample, the final estimation model achieved an accuracy of 90.7%. These results highlight the high estimation accuracy achieved by simultaneously utilizing the approximate volume of the sulcus and the inferior cavity. This is not only highly accurate but also utilizes the sturdier posterior pelvis, making it a promising tool for forensic investigations and the wider field of osteology.

Development of a major amputation prediction model and nomogram in patients with diabetic foot.

Diabetes mellitus, as one of the world's fastest-growing diseases, is a chronic metabolic disease that has now become a public health problem worldwide. The purpose of this research was to develop a predictive nomogram model to demonstrate the risk of major amputation in patients with diabetic foot. A total of 634 Type 2 Diabetes Mellitus (T2DM) patients with diabetic foot ulcer hospitalized at the Air Force Medical Center between January 2018 and December 2023 were included in our retrospective study. There were 468 males (73.82%) and 166 females (26.18%) with an average age of 61.64 ± 11.27years and average body mass index of 24.45 ± 3.56kg/m2. The predictive factors were evaluated by single factor logistic regression and multiple logistic regression and the predictive nomogram was established with these features. Receiver operating characteristic (subject working characteristic curve) and their area under the curve, calibration curve, and decision curve analysis of this major amputation nomogram were assessed. Model validation was performed by the internal validation set, and the receiver operating characteristic curve, calibration curve, and decision curve analysis were used to further evaluate the nomogram model performance and clinical usefulness. Predictors contained in this predictive model included body mass index, ulcer sites, hemoglobin, neutrophil-to-lymphocyte ratio, blood uric acid (BUA), and ejection fraction. Good discrimination with a C-index of 0.957 (95% CI, 0.931-0.983) in the training group and a C-index of 0.987 (95% CI, 0.969-1.000) in the validation cohort were showed with this predictive model. Good calibration were displayed. The decision curve analysis showed that using the nomogram prediction model in the training cohort and validation cohort would respectively have clinical benefits. This new nomogram incorporating body mass index, ulcer sites, hemoglobin, neutrophil-to-lymphocyte ratio, BUA, and ejection fraction has good accuracy and good predictive value for predicting the risk of major amputation in patients with diabetic foot.

Exposure to metal mixtures and telomere length in Bangladeshi children.

Telomere length is associated with chronic diseases and in younger populations, may represent a biomarker of disease susceptibility. As growing evidence suggests that environmental factors, including metals, may impact telomere length, we investigated the association between 17 metals measured in toenail samples and leukocyte relative telomere length (RTL), among 472 five- to seven-year-old children enrolled in the Bangladesh Environmental Research in Children's Health (BiRCH) cohort. In single exposure linear regression models, a doubling of arsenic (As) and mercury (Hg) (μg/g) were associated with a -0.21 (95%CI: -0.032, -0.010; p=0.0005) and -0.017 (95%CI: -0.029, -0.004; p=0.006) difference in RTL, respectively. In Bayesian Kernel Machine Regression (BKMR) mixture models, the overall metal mixture was inversely associated with RTL (P-for-trend <0.001). Negative associations with RTL were observed with both log2-As and log2-Hg, while an inverted U-shaped association was observed for log2-zinc (Zn) with RTL. We found little evidence of interaction among metals. Sex-stratification identified stronger associations of the overall mixture and log2-As with RTL among females, compared to males. Our study suggests that As and Hg may independently influence RTL in mid-childhood. Further studies are needed to investigate potential long-term impacts of metal-associated telomere shortening in childhood on health outcomes in adult life.

Determinants and prevalence of zero-dose children in Somalia: Analysis of the 2020 Health Demographic Survey data.

Despite global progress in childhood vaccination coverage, fragile and humanitarian countries, with high burden of infectious diseases, continue to report a significant number of zero-dose and under-vaccinated children. Efforts to equitably reach zero-dose children remain thus critical. This study assesses the prevalence and determinants of zero-dose children in fragile context of Somalia. We used secondary data from 2020 Somali Health and Demographic Survey (SHDS) to determine status of unvaccinated children aged between 12 to 23 months. Variables related to socio-demographic, household, health seeking, and community level factors were extracted from the SHDS data. Variables that were shown to be significantly associated with zero-dose children at p< 0.05 in the single logistic regression analysis were identified and included in a final multiple logistic regression analysis. A total of 2,304 women and their children aged between 12-23 months were used to determine the prevalence and determinants of zero dose children in Somalia. Approximately 60.2% of the children were zero dose children and did not receive any dose of the four basic routine vaccines. Children living in rural and nomadic areas were more likely to be zero dose (aOR 1.515, 95% CI: 1.189-1.93). Mother with primary education and above (aOR 0.519, 95% CI: 0.371-0.725), those who attended antenatal care (aOR 0.161, 95% CI: 0.124-0.209) and postnatal care (aOR 0.145, 95% CI: 0.085-0.245) and listen frequently to radio (aOR 2.212, 95% CI: 1.106-4.424) were less likely to have children with zero dose than with their counterparts. Majority of children under two years of age in Somalia are reported to be zero dose children. Context and population specific interventions that target vulnerable mothers and their children, in rural and nomadic areas, and from lower wealth quintile index families with no education and adequate access to antenatal and postnatal care remain critical.

Correlation analysis of primal cuts weight, fat contents, and auction prices in Landrace × Yorkshire × Duroc pig carcasses by VCS2000.

Currently, in pork auctions in Korea, only carcass weight and backfat thickness provide information on meat quantity, while the production volume of primal cuts and fat contents remains largely unknown. This study aims to predict the production of primal cuts in pigs and investigate how these carcass traits affect pricing. Using the VCS2000, the production of shoulder blade, loin, belly, shoulder picnic, and ham was measured for gilts (17,257 pigs) and barrows (16,365 pigs) of LYD (Landrace × Yorkshire × Duroc) pigs. Single and multiple regression analysis were conducted to analyze the relationship between the primal cuts and carcass weight. The study also examined the correlation between each primal cut, backfat thickness (1st thoracic vertebra backfat thickness, grading backfat thickness, and Multi-brached muscle middle backfat thickness), pork belly fat percentage, total fat yield, and auction price. A multiple regression analysis was conducted between the carcass traits that showed a high correlation and the auction price. After conducting a single regression analysis on the primal cuts of gilt and barrow, all coefficients of determination (R2) were 0.77 or higher. In the multiple regression analysis, the R2 value was 0.98 or higher. The correlation coefficient between the carcass weights and the auction price exceeded 0.70, while the correlation coefficients between the primal cuts and the auction prices were above 0.65. In terms of fat content, the backfat thickness of gilt exhibited a correlation coefficient of 0.70, and all other items had a correlation coefficient of 0.47 or higher. The correlation coefficients between the Forequarter, Middle, and Hindquarter and the auction price were 0.62 or higher. The R2 values of the multiple regression analysis between carcass traits and auction price were 0.5 or higher for gilts and 0.4 or higher for barrows. The regression equations between carcass weight and primal cuts derived in this study exhibited high determination coefficients, suggesting that they could serve as reliable means to predict primal cut production from pig carcasses. Elucidating the correlation between primal cuts, fat contents and auction prices can provide economic indicators for pork and assist in guiding the direction of pig farming.

Analysis of risk factors related to chronic non-healing wound infection and the construction of a clinical prediction model.

This study is aimed to analyse the risk factors associated with chronic non-healing wound infections, establish a clinical prediction model, and validate its performance. Clinical data were retrospectively collected from 260 patients with chronic non-healing wounds treated in the plastic surgery ward of Shanxi Provincial People's Hospital between January 2022 and December 2023 who met the inclusion criteria. Risk factors were analysed, and a clinical prediction model was constructed using both single and multifactor logistic regression analyses to determine the factors associated with chronic non-healing wound infections. The model's discrimination and calibration were assessed via the concordance index (C-index), receiver operating characteristic (ROC) curve and calibration curve. Multivariate logistic regression analysis identified several independent risk factors for chronic non-healing wound infection: long-term smoking (odds ratio [OR]: 4.122, 95% CI: 3.412-5.312, p < 0.05), history of diabetes (OR: 3.213, 95% CI: 2.867-4.521, p < 0.05), elevated C-reactive protein (OR: 2.981, 95% CI: 2.312-3.579, p < 0.05), elevated procalcitonin (OR: 2.253, 95% CI: 1.893-3.412, p < 0.05) and reduced albumin (OR: 1.892, 95% CI: 1.322-3.112, p < 0.05). The clinical prediction model's C-index was 0.762, with the corrected C-index from internal validation using the bootstrap method being 0.747. The ROC curve indicated an area under the curve (AUC) of 0.762 (95% CI: 0.702-0.822). Both the AUC and C-indexes ranged between 0.7 and 0.9, suggesting moderate-to-good predictive accuracy. The calibration chart demonstrated a good fit between the model's calibration curve and the ideal curve. Long-term smoking, diabetes, elevated C-reactive protein, elevated procalcitonin and reduced albumin are confirmed as independent risk factors for bacterial infection in patients with chronic non-healing wounds. The clinical prediction model based on these factors shows robust performance and substantial predictive value.

Steep medial posterior tibial slope angle and ramp lesion are independent risk factors for an increase in quantitatively measured pivot shift in patients with ACL-deficient knees.

Delayed anterior cruciate ligament (ACL) reconstruction often causes residual anterolateral rotatory instability (ALRI) and consequent knee osteoarthritis, warranting the recommendation of early ACL reconstruction within 6 months after injury. Nonetheless, some cases show notable instability, even shortly after injury. The purpose of this study was to identify risk factors for an increase in quantitatively measured pivot shift in patients with ACL-deficient knees within 6 months after injury. Patients with primary ACL reconstruction within 6 months after injury and quantitative triaxial accelerometer measurements of preoperative pivot shift were included. Descriptive statistics were calculated for 11 independent variables (age, gender, time from injury to surgery, KT-1000, knee extension angle, lateral and medial posterior tibial slope angle, medial and lateral meniscus tears, ramp lesion and Tegner active scale). A single regression analysis was performed on the 11 items and acceleration during the pivot shift, and a multiple regression analysis was performed for items with p value less than 0.1. Overall, 111 patients met the inclusion criteria. Single regression analysis showed that medial posterior tibial slope angle, medial meniscus tear and ramp lesion were significantly correlated with acceleration during the pivot shift (p < 0.001, p = 0.04 and p = 0.001). Multiple regression analysis identified medial posterior tibial slope angles and ramp lesions as independent factors for increased preoperative pivot shift (p = 0.005 and 0.01). A steep medial posterior tibial slope angle and ramp lesion were independent risk factors for increased quantitatively measured pivot shift in patients with ACL-deficient knees. Level IV, case series study.

Bioinformatics prediction and experimental verification identify a cuproptosis-related gene signature as prognosis biomarkers of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) of which its prognostic prediction is still unclarified is a highly heterogeneous disease. Cuproptosis is a form of cell death that depends on copper regulation. Whether the cuproptosis-related genes can be the prognostic indicators of HCC is yet to be elucidated. The aim of this study is to investigate whether cuproptosis-related genes play a role in HCC and can be used as a diagnostic index to predict the occurrence of liver cancer. We downloaded HCC patients' gene expression profiles and their corresponding clinical data from a public database. To screen data, we used single factor Cox regression analysis, meanwhile, polymerase chain reaction (PCR) was used for the verification. After that, the risk score was calculated and the relationship between risk score and clinical factors was analyzed. Besides, a nomogram map was constructed for predicting the prognosis of HCC, and calibration map and decision curve analysis (DCA) map were used to test the model. Compared to the high expression group of four cuproptosis-related genes, the low expression group showed better overall survival (OS) [hazard ratio (HR) =2.58; 95% confidence interval (CI): 1.72-3.89, P<0.01]. The expression of the four cuproptosis-relate genes increased in liver cancer cell lines compared to liver cell lines (P<0.05). Based on these four genes, we calculated the risk score and divided them into two groups as high-risk group and low-risk group. The risk factor map showed the high-risk group had shorter survival time and the four genes were highly expressed. The area under curve (AUC) of receiver operating characteristic (ROC) prediction curve for the first year was 0.726. Risk scores were closely related to clinical factors and immune cells. Finally, we constructed a nomogram for predicting the prognosis of HCC. The risk score for cuproptosis-related genes was established and involved in the construction of the nomogram, providing a new perspective on the prognosis and copper metabolism of HCC.

Multi-factor normalisation of viral counts from wastewater improves the detection accuracy of viral disease in the community

The detection of viruses (e.g. SARS-CoV-2, norovirus) in wastewater represents an effective way to monitor the prevalence of these pathogens circulating within the community. However, accurate quantification of viral concentrations in wastewater, proportional to human input, is constrained by a range of uncertainties, including (i) dilution within the sewer network, (ii) degradation of viral RNA during wastewater transit, (iii) catchment population and facility use, (iv) efficiency of viral concentration and extraction from wastewater, and (v) inhibition of amplification during the RT-qPCR step. Here, we address these uncertainties by investigating several potential normalisation factors including the concentration of ammonium and orthophosphate. A faecal indicator virus (crAssphage), and the recovery of the process-control viruses (murine norovirus and bacteriophage Phi6), used for quality control during the RT-qPCR step, were also considered. We found that multi-factor normalisation of SARS-CoV-2 RT-qPCR data was optimal using a combination of crAssphage, process-control virus recovery, and concentration efficiency to improve prediction accuracy relative to clinical test data. Using multi-normalised SARS-CoV-2 RT-qPCR data, we found a lasso regression model with random forest modelled residuals lowers the prediction error of positives by 46 %, compared to a single linear regression using raw data. This multi-normalised approach enables more accurate wastewater-based predictions of clinical cases up to five days in advance of clinical data, identifying trends in disease prevalence before clinical testing, and demonstrates the potential to improve viral pathogen detection for a range of currently monitored and emerging diseases.

The Extended Anderson and Hauck Tests and Sample Size Procedures for Equivalence Assessment in Simple Linear Regressions

This study describes extended Anderson and Hauck procedures for equivalence testing of slope coefficients and mean responses in one and two regression lines. The general formulation of asymmetric equivalence ranges permits a wide variety of equivalence questions to be tested for a target magnitude or a negligible value. Specifically, the equivalence tests are useful for assessing negligible trend and similar response in a single regression line, and for evaluating unimportant interaction-moderation effect and comparable simple effect between two linear regression lines. The associated power functions and sample size procedures are also derived and compared under the random and fixed model settings. According to the analytic justification and empirical assessment, the exact approaches have a clear advantage over the approximate formulas for accommodating the full stochastic nature of both the response and predictor variables. Computer algorithms are also provided for conducting the proposed equivalence tests, power calculations, and sample size determinations in simple linear regressions.

A Bayesian approach to analyzing long-term agricultural experiments

Effective and flexible statistical analyses are key to getting the most out of long-term experiments (LTEs). Here, we aim to introduce Bayesian analysis to the wider LTE community and show how the modelling process differs from traditional statistical analyses. Bayesian methods have become increasingly popular due to more flexibility in model development with better access to statistical software and sampling algorithms. Using Bayes' Theorem, model coefficients are estimated by incorporating any prior knowledge we may have on model terms. Including prior knowledge in this way requires a different estimating procedure for a fitted model. Bayesian model coefficients are usually sampled from thousands of samples from one or more runs of a Markov Chain. We present the use of Bayesian analyses through three examples. Example 1 illustrates a single regression with and without factors using the Broadbalk Long-Term Experiment, showing how the estimated model changes with more uncertainty in our prior knowledge of model coefficients. Example 2 demonstrates the use of multiple regression, predicting grain yield from factor variables and seasonal weather variables. Example 3 shows an estimation of soil carbon changes under crop rotation and fertilization treatments with a hierarchical time series model using a Swedish soil fertility experiment.

On Least Squares Support Vector Regression for Predicting Mechanical Properties of Steel Rebars

Aiming at ensuring the quality of the product and reducing the cost of steel manufacturing, an increasing number of studies have been developing nonlinear regression models for the prediction of the mechanical properties of steel rebars using machine learning techniques. Bearing this in mind, we revisit this problem by developing a design methodology that amalgamates two powerful concepts in parsimonious model building: (i) sparsity, in the sense that few support vectors are required for building the predictive model, and (ii) locality, in the sense that simpler models can be fitted to smaller data partitions. In this regard, two regression models based on the Least Squares Support Vector Regression (LSSVR) model are developed. The first one is an improved sparse version of the one introduced in a previous work. The second one is a novel local LSSVR-based regression model. The task of interest is the prediction of four output variables (the mechanical properties YS, UTS, UTS/YS, and PE) based on information about its chemical composition (12 variables) and the parameters of the heat treatment rolling (6 variables). The proposed LSSVR-based regression models are evaluated using real-world data collected from steel rebar manufacturing and compared with the global LSSVR model. The local sparse LSSVR approach was able to consistently outperform the standard single regression model approach in the task of interest, achieving improvements in the average R2 from previous studies: 5.04% for UTS, 5.19% for YS, 1.96% for UTS/YS, and 3.41% for PE. Furthermore, the sparsification of the dataset and the local modeling approach significantly reduce the number of SV operations on average, utilizing 34.0% of the total SVs available for UTS estimation, 44.0% for YS, 31.3% for UTS/YS, and 32.8% for PE.

Analysis of risk factors affecting moderate and severe prognosis after discharge in patients with acute cerebral infarction

Objective: To investigate risk factors affecting short-term functional prognosis after discharge in patients with acute cerebral infarction and explore the correlation between relevant factors and National Institutes of Health Stroke Scale (NIHSS) scores. Methods: A retrospective analysis of 4,048 patients with acute cerebral infarction hospitalised between January 2014 and November 2018 in Department of Neurology, Renqiu Kangji Xintu Hospital, Hebei were conducted. The enrolled patients, including 2,506 men and 1,542 women, were divided into mild (n=3,696), moderate (n=278) and severe groups (n=74) based on NIHSS score. Baseline data (gender, history of hypertension, diabetes, hyperlipidaemia, drinking, cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, lipoprotein (a), urea nitrogen, creatinine) were compared among the three groups, and the relationship between relevant factors and NIHSS scores was studied. Results: Through single factor regression analysis, it was found that age, history of stroke, atrial fibrillation, coronary heart disease, antiplatelet medication usage, systolic blood pressure and uric acid were risk factors for the moderate group. Multivariate logistic regression analysis showed that, after adjusting for confounding factors, age, history of stroke and systolic blood pressure (P&lt;0.01) were independent risk factors for the moderate group. Age (OR=1.089; , history of stroke, atrial fibrillation, diastolic blood pressure, fasting plasma glucose and uric acid (P&lt;0.05) were independent risk factors for the severe group. Conclusion: Age, history of stroke, atrial fibrillation, systolic and diastolic blood pressure, fasting plasma glucose and uric acid levels are independent risk factors affecting the short-term post-discharge functional prognosis of patients with acute cerebral infarction and are related to the NIHSS scores of these patients after discharge.

FACTORS INFLUENCING EMPLOYEES JOB PERFORMANCE

Employees performance enables the Employee job performance is key to fortunate key to achieving organizational surviving and goals competition. The present research tested he factors influencing the job performance to ascertain the subjects to work life balance, rewards and Training and development.Design/methodology/approach –This research focuses on the Information Technology industry as single research, using Amos and regression analysis to identify the employees job performance involving the employees of the IT industry in Chennai.Findings– This research shows how Work life balance, Rewards and Training and development will influence the employees job performance in their work place and employees balancing employees as well perform better. This study examines the the connection between employee’s performance and its factors. (Work life balance, Rewards and Training and development)Research limitations/implications– This research contributes to the growing the literature on employee’s job performance and the factors of job performance, this study focuses on IT employees in Chennai.Practical implications – This Research offers IT employees balancing their work and family life and to increase their rewards and training and development, showing the create and good performance on employees their working conditions.Originality/value: This paper gets investigates the factors influencing the job performance of employees. This paper will identify the employees work life balance, Rewards and training and development.

Factors associated with changes in tacrolimus blood concentration after food initiation in patients with ulcerative colitis.

The therapeutic effect of tacrolimus against ulcerative colitis (UC) is correlated with its trough blood concentration. Conventionally, oral tacrolimus for the treatment of UC is initiated under fasting conditions; once the symptoms improve, food intake is resumed. Tacrolimus blood concentration decreases with food intake compared with that under fasting conditions. The aim of this study was to explore the characteristics of patients with UC whose tacrolimus blood concentrations tended to decrease after food initiation. Medical data of 13 patients with UC and treated with tacrolimus were retrospectively obtained. The participant characteristics associated with the changes in tacrolimus blood concentrations after food initiation were analyzed using regression analysis based on the rate of decrease in the concentration/dose (C/D) ratio after food initiation. Single regression analysis showed that the number of days required from tacrolimus initiation to food resumption (P = 0.0071) and individual differences in the increase in tacrolimus blood concentration after administration (P = 0.0247) were significantly associated with the rate of decrease in the C/D ratio after food initiation. Furthermore, multiple regression analysis showed a significant effect of the number of days to food resumption (P = 0.0004) and individual differences in the increase in tacrolimus blood concentration after administration (P = 0.0012). The results suggest that the degree of change in blood tacrolimus concentration after food initiation may be related to the severity of the symptoms and pathology of UC. Early identification of participant characteristics may help control tacrolimus blood concentration fluctuations after food initiation.

Linking the companion diagnostic function of cancer genome profiling to therapy: Investigation of TMB-high predictors.

e13033 Background: Based on the companion diagnostic function of the Comprehensive Cancer Genome Profiling (CGP) test in Japan, pembrolizumab is the standard treatment for patients with Tumor Mutational Burden (TMB)-high (H) solid tumors. In this report, we attempted to extract predictive factors of TMB-H from CGP test results. Methods: We carried out a retrospective cohort study of 42 patients with malignant breast tumors (excluding circulating tumor DNA in the blood) who underwent CGP testing between June 1, 2019, and November 30, 2023, at our hospital and affiliated institutions. We analyzed the association between clinicopathological factors, the number of analyzed cancer-related gene mutations (SNV, InDel), and TMB-H using univariate and multivariate statistical analysis. Results: Of the 42 cases (27 positive and 15 negative for estrogen receptor (ER), respectively), seven (16.7%) were TMB-H. Samples significantly associated with TMB-H according to univariate analysis were submitted as pathology specimens (non-primary (16.7%) vs. primary (0%), p = 0.011), ER (positive (16.7%) vs. negative (0%), p = 0.044), letrozole (LET) (yes (11.9%) vs. no (4.8%), p = 0.008), and previous treatment (after 6th line (14.3%) vs. before (2.4%), p = 0.041). Multivariate analysis showed that LET had a significant effect on TMB-H in Aromatase inhibitor agents (LET + Anastrozole + Exemestane) (LET: OR, 19.9; 95% CI, 2.2–183.5; p = 0.008) and the number of cancer-related gene mutations (PIK3CA+TP53) were significantly affected (PIK3CA: OR, 3.8; 95% CI, 1.1–13.2; p = 0.032. TP53: OR, 0.08; 95% CI, 0.01–0.66; p = 0.015). Single regression analysis showed a significant association between the number of PIK3CA mutations and LET. (β, 0.31; SE, 0.11; p = 0.008). Conclusions: ER positivity, history of LET use, prior therapy after the 6th line, and submission of pathology specimens from non-primary tumors to CGP testing are predictive factors for TMB-H. In multivariate analysis, the history of LET use and the number of PIK3CA mutations were significantly associated with TMB-H. Research shows that PIK3CA mutations are most frequent in patients who have undergone preoperative endocrine therapy with LET (Breast Cancer Res Treat. 2020 Nov;184(1):123–133.), suggesting that an increased number of PIK3CA mutations due to LET contributed to TMB-H. Although immune checkpoint inhibitors (ICIs) are not indicated for ER-positive breast cancer, TMB-H may be a predictor of ICI efficacy in such cases. The limitations of our research were the small sample size, and the study population only included Japanese. For the next steps needed to continue in this research, clinical trials should take place to test whether TMB-H is a predictor of ICIs in a wider population of ER-positive breast cancer.

Lung ultrasound in the assessment of interstitial lung disease in patients with connective tissue disease: Performance in comparison with high-resolution computed tomography.

To investigate clinical relevance of performing lung ultrasound (LUS) in patients with connective tissue disease (CTD)-associated interstitial lung disease (ILD) in comparison with high-resolution computed tomography (HRCT). This single-centre study enrolled eligible patients with CTD-ILD from the prospective LUS registry. Total B-lines were detected by assessment at 14 sites via LUS. Forced vital capacity, diffusing lung capacity for carbon monoxide (DLCO), DLCO/alveolar volume, 6-minute walking distance, and the ILD-GAP index were used as ILD prognostic parameters. Correlations were examined using single and multiple regression analyses. Sixty-seven patients were enrolled, including 29 with idiopathic inflammatory myopathy or anti-synthetase syndrome, 25 with systemic sclerosis (SSc), 10 with rheumatoid arthritis, and 3 with mixed connective tissue disease. The total number of B-lines correlated with ILD extent on HRCT in patients with CTD-ILD (r = 0.66; P < 0.001), particularly in patients with SSc-ILD (r = 0.78; P < 0.001). Total B-lines and ILD extent on HRCT showed comparable correlations with prognostic parameters, while multiple regression analysis revealed the limited benefit of performing LUS in addition to HRCT in predicting correlations with prognostic factors. LUS serves as an alternative tool for assessing the severity and prognosis of patients with CTD-ILD.

Identifying player skill of dota 2 using machine learning pipeline

The esports industry is one of the prominent business sectors in the digital era, particularly, Multiplayer Online Battle Arena (MOBA) games which gain much attention from gamers and streaming audiences. Among such games, Defense of the Ancient 2 or Dota 2 is the record holder for the highest prize esports tournament. Therefore, various companies and investors start their esports teams to compete in the Dota 2 tournaments, the Internationals. To success in the competition, player recruitment is a crucial process as it usually takes considerable effort to find a skillful player. Watching the game replay to evaluate the player’s skill is one of the approaches. However, it can be too exhaustive, also some player’s ranking, which represent the player’s skill, are often not available. In this paper, we propose an effective machine learning pipeline to evaluate the player’s skill. Our designed pipeline includes data collection, feature engineering, and machine learning modeling. We show the data collection process using open-source API. An effective method for feature engineering is proposed. Features, e.g., end-game, or tactical decision related statistics, are incorporated along with the resource in the game distribution, harassment tactic, or spatiotemporal features, in order to provide effective models. Subsequently, we apply major machine learning models based on a single game data, i.e., logistic regression and random forest, to the processed data. The most effective model can achieve up to 0.7091 precision, 0.5850 recall, 0.6411 F1-score, and 0.8123 ROC AUC score.