Selection Of Candidates Research Articles

Italian Society of Interventional Cardiology (GISE) and Italian Society of Arterial Hypertension (SIIA) Position Paper on the role of renal denervation in the management of the difficult-to-treat hypertension.

Renal denervation (RDN) is a safe and effective strategy for the treatment of difficult to treat hypertension. The blood pressure (BP)-lowering efficacy of RDN is comparable to those of many single antihypertensive medications and it allows to consider the RDN as a valuable option for the treatment of difficult to treat hypertension together with lifestyle modifications and medical therapy. A multidisciplinary team is of pivotal importance from the selection of the patient candidate for the procedure to the post-procedural management. Further studies are needed to investigate the effect of RDN on clinical outcomes and to better identify the predictors of BP response to RDN in order to recognize the patients who are more likely to benefit from the procedure.

Absence of radio-bright dominance in a near-infrared selected sample of red quasars

Context. The dichotomy between red and blue quasars is still an open question. It is debated whether red quasars are simply blue quasars that are observed at certain inclination angles or if they provide insight into a transitional phase in the evolution of quasars. Aims. We investigate the relation between quasar colors and radio-detected fraction because radio observations of quasars provide a powerful tool in distinguishing between quasar models. Methods. We present the eHAQ+GAIA23 sample, which contains quasars from the High A(V) Quasar (HAQ) Survey, the Extended High A(V) Quasar (eHAQ) Survey, and the Gaia quasar survey. All quasars in this sample have been found using a near-infrared color selection of target candidates that have otherwise been missed by the Sloan Digital Sky Survey (SDSS). We implemented a redshift-dependent color cut in g* − i* to select red quasars in the sample and divided them into redshift bins, while using a nearest-neighbors algorithm to control for luminosity and redshift differences between our red quasar sample and a selected blue sample from the SDSS. Within each bin, we cross-matched the quasars to the Faint Images of the Radio Sky at Twenty centimeters (FIRST) survey and determined the radio-detection fraction. Results. For redshifts 0.8 < z ≤ 1.5, the red and blue quasars have a radio-detection fraction of 0.153−0.032+0.037 and 0.132−0.030+0.034, respectively. The red and blue quasars with redshifts 1.5 < z ≤ 2.4 have radio-detection fractions of 0.059−0.016+0.019 and 0.060−0.016+0.019, respectively, and the red and blue quasars with redshifts z > 2.4 have radio-detection fractions of 0.029−0.012+0.017 and 0.058−0.019+0.024, respectively. For the WISE color-selected red quasars, we find a radio-detection fraction of 0.160−0.034+0.038 for redshifts 0.8 < z ≤ 1.5, 0.063−0.017+0.020 for redshifts 1.5 < z ≤ 2.4, and 0.051−0.022+0.030 for redshifts z > 2.4. In other words, we find similar radio-detection fractions for red and blue quasars within < 1σ uncertainty, independent of redshift. This disagrees with what has been found in the literature for red quasars in SDSS. It should be noted that the fraction of broad absorption line (BAL) quasars in red SDSS quasars is about five times lower. BAL quasars have been observed to be more frequently radio quiet than other quasars, therefore the difference in BAL fractions could explain the difference in radio-detection fraction. Conclusions. The dusty torus of a quasar is transparent to radio emission. When we do not observe a difference between red and blue quasars, it leads us to argue that orientation is the main cause of quasar redness. Moreover, the observed higher proportion of BAL quasars in our dataset relative to the SDSS sample, along with the higher rate of radio detections, indicates an association of the redness of quasars and the inherent BAL fraction within the overall quasar population. This correlation suggests that the redness of quasars is intertwined with the inherent occurrence of BAL quasars within the entire population of quasars. In other words, the question why some quasars appear red or exhibit BAL characteristics might not be isolated; it could be directly related to the overall prevalence of BAL quasars in the quasar population. This finding highlights the need to explore the underlying factors contributing to both the redness and the frequency of BAL quasars, as they appear to be interconnected phenomena.

Boost recall in quasi-stellar object selection from highly imbalanced photometric datasets

Context. The identification of bright quasi-stellar objects (QSOs) is of fundamental importance to probe the intergalactic medium and address open questions in cosmology. Several approaches have been adopted to find such sources in the currently available photometric surveys, including machine learning methods. However, the rarity of bright QSOs at high redshifts compared to other contaminating sources (such as stars and galaxies) makes the selection of reliable candidates a difficult task, especially when high completeness is required. Aims. We present a novel technique to boost recall (i.e., completeness within the considered sample) in the selection of QSOs from photometric datasets dominated by stars, galaxies, and low-z QSOs (imbalanced datasets). Methods. Our heuristic method operates by iteratively removing sources whose probability of belonging to a noninteresting class exceeds a user-defined threshold, until the remaining dataset contains mainly high-z QSOs. Any existing machine learning method can be used as the underlying classifier, provided it allows for a classification probability to be estimated. We applied the method to a dataset obtained by cross-matching PanSTARRS1 (DR2), Gaia (DR3), and WISE, and identified the high-z QSO candidates using both our method and its direct multi-label counterpart. Results. We ran several tests by randomly choosing the training and test datasets, and achieved significant improvements in recall which increased from ~50% to ~85% for QSOs with z > 2.5, and from ~70% to ~90% for QSOs with z > 3. Also, we identified a sample of 3098 new QSO candidates on a sample of 2.6 ×106 sources with no known classification. We obtained follow-up spectroscopy for 121 candidates, confirming 107 new QSOs with z > 2.5. Finally, a comparison of our QSO candidates with those selected by an independent method based on Gaia spectroscopy shows that the two samples overlap by more than 90% and that both selection methods are potentially capable of achieving a high level of completeness.

Venoarterial extracorporeal membrane oxygenation in immunocompromised patients with cardiogenic shock: a cohort study and propensity-weighted analysis.

The outcomes of immunocompromised patients with cardiogenic shock treated with venoarterial extracorporeal membrane oxygenation (VA-ECMO) are seldom documented, making ECMO candidacy decisions challenging. This study aims (1) to report outcomes of immunocompromised patients treated with VA-ECMO, (2) to identify pre-ECMO predictors of 90-day mortality, (3) to assess the impact of immunodepression on 90-day mortality, and (4) to describe the main ECMO-related complications. This is a retrospective, propensity-weighted study conducted in two French experienced ECMO centers. From January 2006 to January 2022, 177 critically ill immunocompromised patients (median (interquartile range, IQR) age 49 (32-60) years) received VA-ECMO. The main causes of immunosuppression were long-term corticosteroids/immunosuppressant treatment (29%), hematological malignancy (26%), solid organ transplant (20%), and solid tumor (13%). Overall 90-day and 1-year mortality were 70% (95% confidence interval (CI) 63-77%) and 75% (95% CI 65-79%), respectively. Older age and higher pre-ECMO lactate were independently associated with 90-day mortality. Across immunodepression causes, 1-year mortality ranged from 58% for patients with infection by human immunodeficiency virus (HIV) or asplenia, to 89% for solid organ transplant recipients. Hemorrhagic and infectious complications affected 39% and 54% of patients, while more than half the stay in intensive care unit (ICU) was spent on antibiotics. In a propensity score-weighted model comparing the 177 patients with 942 non-immunocompromised patients experiencing cardiogenic shock on VA-ECMO, immunocompromised status was independently associated with a higher 90-day mortality (odds ratio 2.53, 95% CI 1.72-3.79). Immunocompromised patients undergoing VA-ECMO treatment face an unfavorable prognosis, with higher 90-day mortality compared to non-immunocompromised patients. This underscores the necessity for thorough evaluation and careful selection of ECMO candidates within this frail population.

Consider the Base for Representation at the Top: Political Parties and Women’s Parliamentary Representation in Ghana’s Fourth Republic

Abstract: In a democracy, political parties play significant roles in candidate recruitment and selection. While several activities of political parties focusing on boosting women’s representation in Ghana’s Fourth Republic have been examined in the literature, this study expands that discussion. It notes that political parties have pursued various strategies to increase women’s political representation; however, after three decades of Ghana’s Fourth Republic, parliamentary seats occupied by women fall short of the minimum 30 percent level suggested by the UN Economic and Social Council resolution 1990/15. This suggests that perhaps Ghana is not doing enough. Using a qualitative research approach, this study argues that political parties must pursue women’s recruitment with greater proactiveness from the basic unit of their political structure, the polling station or branch executives’ level. This could encourage more female participation and influence the outcome of primary elections.

Traits of insect herbivores and target weeds associated with greater biological weed control establishment and impact

AbstractImproving success rates of classical weed biocontrol programs is an ongoing effort that requires a variety of different approaches. Previous assessments indicated biocontrol agent taxonomy and feeding characteristics and weed life history traits are associated with better control outcomes. We examined weed biocontrol releases for correlations between biocontrol agent and target weed traits associated with different levels of reported establishment and control. Data collated in the 5th edition of ‘Biological Control of Weeds: A World Catalogue of Agents and Their Target Weeds’ were used as the basis for this global analysis. Published literature was used to augment the catalog with data for eight biocontrol agent traits and four target weed traits. Biocontrol agent establishment and impact data were analyzed against these traits using generalized linear mixed models and categorical models, respectively. Analyses for biocontrol agent establishment reveal the following agent traits were correlated with a greater probability of establishment: being an internal feeder, feeding on above-ground plant tissues, multivoltine agents and agents that feed during both their adult and immature life stages. Insect taxon did not affect establishment except for the order Lepidoptera, which had the lowest establishment probability. For weed traits, those occurring in aquatic or riparian habitats were associated with a higher probability of biocontrol agent establishment. Regarding agent impact, using the definition categories in the catalog, agents feeding externally and on vegetative plant tissues, multivoltine agents and those with both adult and immature plant-feeding life stages were strongly correlated with greater impact. Perennials, reproducing only vegetatively and invading aquatic or riparian habitats were associated with greater biocontrol impact. Our findings could facilitate both the prioritization of invasive plants targeted for biocontrol and the selection of suitable biocontrol agent candidates, which should further improve biocontrol project outcomes.

Physical functional recovery in the early period after hospitalization for COVID-19: impact of hypertension and outcome prediction model

The aim – to study the influence of hypertension (HT) on the dynamics of natural recovery of physical functional status in the early period after hospitalization for COVID-19 and to develop a model for predicting recovery results at 1 month after discharge.Materials and methods. 221 hospitalized patients with COVID-19 (age 53.4±13.6 years, 53 % women) were included in the study, 176 of whom underwent the 6-minute walk test (6MWT) using an extended protocol within 1-2 days before discharge. A repeat visit to assess the dynamics of natural recovery was performed at 1 month after discharge.Results and discussion. Pre-discharge 6-minute walk distance was 378±57 m in patients with HT and 418±75 m without it, p=0.001, during the second visit – 440±52 versus 478±68, p=0.002; the achieved percentage of the individually predicted distance was 67.4±10.5 vs. 69.5±13.6 % and 81.6±9.9 vs. 81.9±15.7 %, respectively, p>0.05 for both visits. The increase in heart rate during the test at visit 1 was 18.5±8.3 versus 30.1±19.3 bpm, p<0.001, the percentage of chronotropic reserve utilizatoin was 21.3±9.6 % versus 29.2±11.4 %, p<0.001. During the second visit, residual manifestations of this trend were observed, with an increase in HR by 24.0±9.5 vs. 30.8±12.1, p=0.003 and the use of chronotropic reserve of 28.1±10.1 % vs. 33.4±12.4 %, respectively, p=0.029. The developed multivariate linear regression model explained 59 % of the variability in the achieved percentage of the individually predicted 6-minute walk distance at 1 month after discharge. The use of machine learning allowed to create an artificial neural network based regression model that used age, height, use of remdesivir in treatment, and SBP and DBP values at the time of discharge as predictors, and explained 90 % of observed variability.Conclusions. Hospitalized patients with COVID-19 were characterized by a decrease in the general physical functional status as assessed by 6MWT at the time of discharge and incomplete recovery after 1 month. Presence of hypertension was associated with more pronounced disturbances of the autonomic regulation of heart rate, but did not affect the reached percentage of the distance walked. The proposed artificial neural network based regression model allows for a high accuracy prediction of the 6MWT results at 1 month after discharge, which can be used in the selection of candidates for cardiopulmonary rehabilitation programs.

Molecular guidelines for promising antimicrobial agents

Antimicrobial resistance presents a pressing challenge to public health, which requires the search for novel antimicrobial agents. Various experimental and theoretical methods are employed to understand drug-target interactions and propose multistep solutions. Nonetheless, efficient screening of drug databases requires rapid and precise numerical analysis to validate antimicrobial efficacy. Diptool addresses this need by predicting free energy barriers and local minima for drug translocation across lipid membranes. In the current study employing Diptool free energy predictions, the thermodynamic commonalities between selected antimicrobial molecules were characterized and investigated. To this end, various clustering methods were used to identify promising groups with antimicrobial activity. Furthermore, the molecular fingerprinting and machine learning approach (ML) revealed common structural elements and physicochemical parameters in these clusters, such as long carbon chains, charged ammonium groups, and low dipole moments. This led to the establishment of guidelines for the selection of effective antimicrobial candidates based on partition coefficients (logP) and molecular mass ranges. These guidelines were implemented within the Reinforcement Learning for Structural Evolution (ReLeaSE) framework, generating new chemicals with desired properties. Interestingly, ReLeaSE produced molecules with structural profiles similar to the antimicrobial agents tested, confirming the importance of the identified features. In conclusion, this study demonstrates the ability of molecular fingerprinting and AI-driven methods to identify promising antimicrobial agents with a broad range of properties. These findings deliver substantial implications for the development of antimicrobial drugs and the ongoing battle against antibiotic-resistant bacteria.

An integrative mechanistic model of thymocyte dynamics.

The thymus plays a central role in shaping human immune function. A mechanistic, quantitative description of immune cell dynamics and thymic output under homeostatic conditions and various patho-physiological scenarios are of particular interest in drug development applications, e.g., in the identification of potential therapeutic targets and selection of lead drug candidates against infectious diseases. We here developed an integrative mathematical model of thymocyte dynamics in human. It incorporates mechanistic features of thymocyte homeostasis as well as spatial constraints of the thymus and considerations of age-dependent involution. All model parameter estimates were obtained based on published physiological data of thymocyte dynamics and thymus properties in mouse and human. We performed model sensitivity analyses to reveal potential therapeutic targets through an identification of processes critically affecting thymic function; we further explored differences in thymic function across healthy subjects, multiple sclerosis patients, and patients on fingolimod treatment. We found thymic function to be most impacted by the egress, proliferation, differentiation and death rates of those thymocytes which are most differentiated. Model predictions also showed that the clinically observed decrease in relapse risk with age, in multiple sclerosis patients who would have discontinued fingolimod therapy, can be explained mechanistically by decreased thymic output with age. Moreover, we quantified the effects of fingolimod treatment duration on thymic output. In summary, the proposed model accurately describes, in mechanistic terms, thymic output as a function of age. It may be further used to perform predictive simulations of clinically relevant scenarios which combine specific patho-physiological conditions and pharmacological interventions of interest.

Utilizing murine dendritic cell line DC2.4 to evaluate the immunogenicity of subunit vaccines in vitro.

Subunit vaccines hold substantial promise in controlling infectious diseases, due to their superior safety profile, specific immunogenicity, simplified manufacturing processes, and well-defined chemical compositions. One of the most important end-targets of vaccines is a subset of lymphocytes originating from the thymus, known as T cells, which possess the ability to mount an antigen-specific immune response. Furthermore, vaccines confer long-term immunity through the generation of memory T cell pools. Dendritic cells are essential for the activation of T cells and the induction of adaptive immunity, making them key for the in vitro evaluation of vaccine efficacy. Upon internalization by dendritic cells, vaccine-bearing antigens are processed, and suitable fragments are presented to T cells by major histocompatibility complex (MHC) molecules. In addition, DCs can secrete various cytokines to crosstalk with T cells to coordinate subsequent immune responses. Here, we generated an in vitro model using the immortalized murine dendritic cell line, DC2.4, to recapitulate the process of antigen uptake and DC maturation, measured as the elevation of CD40, MHC-II, CD80 and CD86 on the cell surface. The levels of key DC cytokines, tumor necrosis alpha (TNF-α) and interleukin-10 (IL-10) were measured to better define DC activation. This information served as a cost-effective and rapid proxy for assessing the antigen presentation efficacy of various vaccine formulations, demonstrating a strong correlation with previously published in vivo study outcomes. Hence, our assay enables the selection of the lead vaccine candidates based on DC activation capacity prior to in vivo animal studies.

Twitter Data Sentimen Analysis 2024 Presidential Candidate Using Algorithm Naïve Bayes Classifier By Methods K-Fold Cross Validation

Indonesia implements a democratic system by involving the public in General Elections (Pemilu) for specific political positions. The active community expresses opinions on social media, especially regarding the 2024 Presidential Election (Pilpres) and respective presidential candidates, which have become trending topics on Twitter. The analysis used to absorb these tweets into information is sentimen analysis using the Naïve Bayes Classifier algorithm with the K-fold Cross-Validation method. Through the stages of pre-processing, weighting, labeling, classification using NBC, and testing using a Confusion Matrix, The results of the classification from NBC showed that Anies got 80% positive tweets and 20% negative tweets from 1186 tweets, Prabowo Subianto got 78% positive tweets and 22% negative tweets from 1149 tweets, and Ganjar Pranowo got 77% positive tweets and 23% negative tweets from 1075 tweets. Testing process was carried out using the NBC algorithm with the K-Fold Cross Validation method using values k=1 to k=10. The function of K-Fold Cross Validation is to maximize the confusion matrix result. It can be conclude that Anies Baswedan has the highest score in iteration 4, namely a precision value of 90%, a recall value of 99%, and the accurary value of 91%. Furthemore, Ganjar Pranowo had the highest score in iteration 9, namely a precision value of 95%,a recall value of 97%, and an accuracy value of 92%. Meanwhile, Prabowo Subianto had the highest score in iteration 9, namely a precision value of 97%, a recall value of 99%, and an accuracy value of 94%.

Venetoclax with decitabine or azacitidine in relapsed or refractory acute myeloid leukemia.

Relapsed or refractory acute myeloid leukemia (AML) is associated with poor outcomes and resistance to therapy. The addition of venetoclax, a BCL-2 antagonist, to lower-intensity therapies results in improved survival in the first-line setting compared to monotherapy with a hypomethylating agent or low-dose cytarabine. Despite this, much remains unknown about the performance of venetoclax with a hypomethylating agent following the first-line setting. Additionally, while the ELN 2022 guidelines appear to improve the prognostication of AML, clarification is needed to determine how the revision applies to lower-intensity strategies. To investigate this, we retrospectively analyzed the performance of venetoclax with decitabine or azacitidine in relapsed or refractory AML under the ELN 2022 guidelines. We demonstrated that the ELN 2022 revision is not optimized for lower-intensity venetoclax-based strategies. To refine the prognostication schema, we showed significantly improved response and survival benefits for patients with mutated NPM1 and IDH. Relatively, patients with mutated NRAS, KRAS, and FLT3-ITD were associated with inferior response and survival. Furthermore, there is an unmet clinical need for tools to improve the selection of lower-intensity therapy candidates with borderline functional status. Using an incremental survival computation method, we discovered that a CCI score threshold of 5 distinguishes patients at an elevated risk of death. Together, these novel findings highlight areas of refinement to improve survival in relapsed or refractory AML.

The Future of Food Allergy Management: Advancements in Therapies.

To review current and future treatment options for IgE-mediated food allergy. Recent years have seen major developments in both allergen-specific and allergen-non-specific treatment options, with the first FDA-approved peanut oral immunotherapy (OIT) product becoming available in 2020. In addition to OIT, other immunotherapy modalities, biologics, adjunct therapies, and novel therapeutics are under investigation. Food allergy is a potentially life-threatening condition associated with a significant psychosocial impact. Numerous products and protocols are under investigation, with most studies focusing on OIT. A high rate of adverse events, need for frequent office visits, and cost remain challenges with OIT. Further work is needed to unify outcome measures, develop treatment protocols that minimize adverse events, establish demographic and clinical factors that influence candidate selection, and identify patient priorities.

The role of hypertensive status in the preoperative patient selection of TSA

This study investigates the relationship between hypertension and postoperative complications following total shoulder arthroplasty (TSA). All patients who underwent TSA between 2015 and 2020 from the American College of Surgeons National Surgical Quality Improvement database were surveyed. The study population was divided into patients with no hypertension and patients with hypertension. Patient demographics, comorbidities, and 30-day postoperative complications were collected. Logistic regression analysis was used to investigate the relationship between hypertension and postoperative complications. Compared to no hypertension, hypertension was significantly associated with an increased likelihood of experiencing sepsis (P=.021), pneumonia (P=.019), myocardial infarction (P=.038), blood transfusions (P=.006), readmission (P<.001), reoperation (P<.001), non-home discharge (P<.001), and any complication (P<.001). After accounting for significant patient variables, compared to no hypertension, hypertension was independently significantly associated with an increased likelihood of experiencing reoperation (odds ratio 1.48; 95% CI, 1.142-1.905; P=.003) and any complication (odds ratio 1.10; 95% CI, 1.008-1.205; P=.033). In this study, we identified hypertension as an independent significant predictor for both reoperation and any complication following TSA. This study provides evidence for incorporating a patient's hypertensive status into preoperative screening, aiming to improve surgical candidate selection and surgical outcomes following TSA.

Role and challenges of ChatGPT, Gemini, and similar generative artificial intelligence in human resource management

The integration of generative artificial intelligence (AI) systems, such as ChatGPT, into Human Resource Management (HRM) has marked the beginning of a groundbreaking era in innovative workforce management and employee engagement. This research investigates the pivotal role played by ChatGPT and analogous generative AI technologies in HRM, underscoring their significance in the realms of recruitment, employee training, and organizational communication. Leveraging their natural language processing abilities, these AI systems streamline the recruitment process, ensuring unbiased candidate selection and enhancing the overall efficiency of HR departments. Moreover, in training and development initiatives, ChatGPT facilitates tailored learning experiences, adjusting content to meet individual employee needs, thereby fostering skill enhancement and professional growth. Nevertheless, the widespread adoption of ChatGPT in HRM is not without its challenges. Ethical concerns, such as data privacy and algorithmic bias, necessitate thorough examination to prevent discriminatory practices and guarantee equitable treatment of employees. Additionally, the requirement for continuous monitoring and refinement of AI algorithms to align with evolving organizational cultures and goals presents a significant hurdle. Moreover, striking a harmonious balance between AI-driven automation and human intervention is imperative to preserve the human touch in HRM processes, safeguarding the empathetic and intuitive elements that are essential for effective employee management. This research delves into these complex dynamics, shedding light on the transformative potential of ChatGPT and akin generative AI technologies in HRM, while also emphasizing the need for vigilance and strategic planning to address the associated challenges. Through an exhaustive analysis of real-world case studies and ethical frameworks, this study offers valuable insights for HR professionals, policymakers, and researchers endeavoring to navigate the intricate landscape of AI-powered HRM.&#x0D;

Unpacking candidate selection in authoritarian regimes: Evidence from Cameroon

Why do autocratic ruling parties adopt certain candidate selection processes, and what effects do they have on political outcomes? This article argues that many contemporary autocracies democratize candidate selection to address the demands of multiparty elections, but that this creates unforeseen hazards. Autocrats centralize candidate selection to reassert control over the composition of legislatures, but not necessarily to recruit more effective representatives. These ideas are explored in Cameroon, a longstanding authoritarian regime that has altered its candidate selection twice. Using an original biographical dataset and elite survey that measures legislative role orientations, the article shows how changes to candidate selection allowed the Cameroonian regime to retain more partisan legislators and to recruit new candidates who were more oriented to the party than the constituency. The article expands the comparative study of candidate selection to new settings and provides insights into how candidate selection helps shape and sustain authoritarian regimes.

Factors Affecting Selection of TraineE for Neurointervention (FASTEN).

Neurointervention is a very competitive specialty in the United States due to the limited number of training spots and the larger pool of applicants. The training standards are continuously updated to ensure solid training experiences. Factors affecting candidate(s) selection have not been fully established yet. Our study aims to investigate the factors influencing the selection process. A 52-question survey was distributed to 93 program directors (PDs). The survey consisted of six categories: (a) Program characteristics, (b) Candidate demographics, (c) Educational credentials, (d) Personal traits, (e) Research and extracurricular activities, and (f) Overall final set of characteristics. The response rate was 59.1%. As per the programs' characteristics, neurosurgery was the most involved specialty in running the training programs (69%). Regarding demographics, the need for visa sponsorship held the greatest prominence with a mean score of 5.9 [standard deviation (SD) 2.9]. For the educational credentials, being a graduate from a neurosurgical residency and the institution where the candidate's residency training is/was scored the highest [5.4 (SD = 2.9), 5.4 (SD = 2.5), respectively]. Regarding the personal traits, assessment by faculty members achieved the highest score [8.9 (SD = 1)]. In terms of research/extracurricular activities, fluency in English had the highest score [7.2 (SD = 1.9)] followed by peer-reviewed/PubMed-indexed publications [6.4 (SD = 2.2)]. Our survey investigated the factors influencing the final decision when choosing the future neurointerventional trainee, including demographic, educational, research, and extracurricular activities, which might serve as valuable guidance for both applicants and programs to refine the selection process.

Who benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-dimensional model for candidate selection

BackgroundDespite promising overall survival of stage I lung adenocarcinoma (LUAD) patients, 10-25 % of them still went through recurrence after surgery. [1] While it is still disputable whether adjuvant chemotherapy is necessary for stage I patients. [2] IASLC grading system for non-mucinous LUAD shows that minor high-grade patterns are significant indicator of poor prognosis. [3] Other risk factors, such as, pleura invasion, lympho-vascular invasion, STAS, etc. are also related to poor prognosis. [4-6] There still lack evidence whether IASLC grade itself or together with other risk factors can guide the use of adjuvant therapy in stage I patients. In this article, we tried to establish a multi-variable recurrence prediction model for stage I LUAD patients that is able to identify candidates of adjuvant chemotherapy. MethodsWe retrospectively collected patients who underwent lung surgery from 2018.8.1 to 2018.12.31 at our institution and diagnosed with lung adenocarcinoma pT1-2aN0M0 (stage I). Clinical data, manifestation on CT scan, pathologic features, driver gene mutations and follow-up information were collected. Cox proportional hazards regression analyses were performed utilizing the non-adjuvant cohort to predict disease free survival (DFS) and a nomogram was constructed and applied to the total cohort. Kaplan-Meier method was used to compare DFS between groups. Statistical analysis was conducted by R version 3.6.3. FindingsA total of 913 stage I LUAD patients were included in this study. Median follow-up time is 48.1 months.4-year and 5-year DFS are 92.9 % and 89.6 % for the total cohort. 65 patient experienced recurrence or death. 4-year DFS are 97.0 %,94.6 % and 76.2 %, and 5-year DFS are 95.5 %, 90.0 % and 74.1 % in IASLC Grade1, 2 and 3, respectively(p < 0.0001). High-risk patients defined by single risk factors, such as, IASLC grade 3, pleura invasion, STAS, less LN resected could not benefit from adjuvant therapy. A LASSO-COX regression model was built and patients are divided into high-risk and low-risk groups. In the high-risk group, patients underwent adjuvant chemotherapy have longer DFS than those who did not (p = 0.024), while in the low-risk group, patients underwent adjuvant chemotherapy have inferior DFS than those who did not (p < 0.001). InterpretationIASLC grading is a significant indicator of DFS, however it could not guide adjuvant therapy in our stage I LUAD cohort. Growth patterns and T indicators together with other risk factors could identify high-risk patients that are potential candidate of adjuvant therapy, including some stage IA LUAD patients.

Responsible machine learning for United States Air Force pilot candidate selection

The United States Air Force (USAF) continues to be plagued by a chronic pilot shortage, one that could be exacerbated by an accompanying shortfall in the commercial airlines. As a result, efforts have increased to alleviate this shortage by finding methods to reduce pilot training attrition. We contribute to these efforts by setting forth a decision support system (DSS) for pilot candidate selection using modern machine learning techniques. In view of the recent Responsible Artificial Intelligence Strategy published by the United States Department of Defense, this research leverages interpretable and explainable machine learning methods to create traceable and equitable models that may be responsibly and reliably governed. These models are used to regress candidates’ average merit assignment selection system scores based on information available for selection and prior to training. More specifically, using data provided by the USAF from 2010 to 2018, this paper develops and analyzes multiple interpretable models based on Gaussian Bayesian networks, as well as multiple black-box models rendered explainable by SHAP values and conformal prediction. A preferred pair of interpretable and explainable models is selected and embedded within a DSS for USAF pilot candidate selection boards: the Air Force Pilot Applicant Selection System. The utilization of this DSS is explored, the analyses it enables are discussed, and relevant USAF policymaking issues are examined.

Baseline Severity and Disease Duration Can Predict the Response to Allergen-specific Immunotherapy in Allergic Rhinitis.

Allergen-specific immunotherapy (AIT) has confirmed its efficacy in improving the symptoms of allergic rhinitis. However, no reliable biomarkers have been identified to predict the efficacy of AIT were found. We aimed to find clinical and immunological markers to predict efficacy in children after 2 years of sublingual immunotherapy (SLIT). A total of 285 children diagnosed with allergic rhinitis were recruited. The clinical efficacy was evaluated by comparing endpoint and baseline symptom and medication scores (SMS). Baseline clinical and immunological markers (serum total and specific immunoglobulin [Ig]E) and their correlation with clinical efficacy were analyzed. Of the 285 children recruited, 249 completed the 2-year SLIT program. After 2 years of SLIT, 68.3% of the children showed a significant response. Children in the Remarkable Response Group had the highest baseline SMS and most extended disease duration, followed by the Effective Relief and Unresponsive Group. Correlation analysis demonstrated that SMS improvement was positively correlated with baseline SMS (r=0.67) and disease duration (r=0.35). SMS improvement was not correlated with age, body mass index, total or specific IgE levels, or their ratios. Our results show that baseline SMS and disease duration can predict the efficacy of SLIT. Our study can guide the selection of suitable candidates for SLIT.