Personal Assessment Research Articles

Post-transplant complications revealed by mycophenolate mofetil related transporters and metabolic enzymes gene polymorphisms in pediatric patients with hematological disorders

BackgroundHaploidentical hematopoietic stem cell transplantation (Haplo-HSCT) serves as an important option for patients without an HLA matched donor in treating hematological disorders, while patients may experience various complications after transplantation. Mycophenolate mofetil (MMF), a cornerstone drug for graft-versus-host disease (GvHD) prophylaxis, effectively reduces the incidence of acute GvHD, and the efficacy of MMF varies among individuals associated with MMF-related transporters and metabolic enzymes single nucleotide polymorphisms (SNPs). However, limited studies have systematically reported the correlations between the MMF-related SNPs and post-transplant complications.MethodsHere, we conducted a retrospective study involving 90 pediatric patients with hematological disorders who underwent haplo-HSCT at a single center. All patients were subjected to MMF-related SNP testing, combined with common clinical characteristics, to be correlated with post-transplant complications.ResultsWe observed that all 15 MMF-related SNPs were in Hardy-Weinberg equilibrium. Based on multivariate Cox regression analysis of post-transplant complications, we discovered that SLCO1B1 (521T > C) variant genotype was an independent protective factor for chronic GvHD (HR = 0.25, 95% confidence interval (CI) (0.08–0.84)). For viral infection, CYP2C8 (1291 + 106T > C) variant genotype was an independent risk factor for cytomegalovirus infection (HR = 2.98, 95% CI (1.18–7.53)). As to hemorrhagic cystitis, SLCO1B1 (1865 + 4846T > C) variant genotype was an independent protective factor, while older age was considered as an independent risk factor (HR = 0.41, 95% CI (0.19–0.85); HR = 2.52, 95% CI (1.14–5.54), respectively). No statistical significance was discovered between common clinical characteristics and MMF-related SNPs with other complications, including grade II-IV/III-IV acute GvHD, Epstein-Barr virus infection, peri-engraftment syndrome, and capillary leak syndrome. We also discovered SLCO1B1 (597 C > T) and SLC29A1 (-162 + 228 A > C) variant genotypes are both independent factors for cumulative incidence of relapse after haplo-HSCT (HR = 4.02, 95% CI (1.42–11.44); HR = 0.18, 95% CI (0.07–0.43), respectively).ConclusionsOur findings highlight the significance of MMF-related transporters and metabolic enzymes SNPs in the development of post-transplant complications, contributing to facilitating personalized risk assessment and improving the clinical management in haplo-HSCT patients.

Legal Uncertainty for Persons with Disabilities as Victims and Witnesses in Criminal Justice in Indonesia

This study examines the legal uncertainty faced by persons with disabilities as witnesses and victims within Indonesia's criminal justice system. Although there are regulations that acknowledge the rights of persons with disabilities, their implementation often encounters various obstacles, ranging from inadequate accommodations to limited understanding among law enforcement officials. This legal uncertainty adversely impacts access to justice for persons with disabilities, who often feel marginalized within the legal process. Therefore, this research also discusses efforts to fulfill the rights of persons with disabilities as witnesses and victims, including the importance of training for law enforcement personnel, providing appropriate accommodations, and conducting comprehensive personal assessments to meet their specific needs. This study is expected to provide recommendations for policymakers to enhance legal protection and ensure accessibility for persons with disabilities within the criminal justice system.

Mitigating and managing infection risk in adults treated with CAR T-cell therapy.

Chimeric antigen receptor T-cell therapy (CAR-T) has transformed the treatment paradigm of relapsed/refractory B-cell malignancies. Yet, this therapy is not without toxicities. While the early inflammation-mediated toxicities are now better understood, delayed hematopoietic recovery and infections result in morbidity and mortality risks that persist for months following CAR-T. The predisposition to infections is a consequence of immunosuppression from the underlying disease, prior therapies, lymphodepletion chemotherapy, delayed hematopoietic recovery, B-cell aplasia, and delayed T-cell immune reconstitution. These risks and epidemiology can vary over a post-CAR-T timeline of early (<30 days), prolonged (30-90 days), or late (>90 days) follow-up. Antibacterial, antiviral, and antifungal prophylaxis; growth factors and stem cell boost to expedite count recovery; immunoglobulin replacement therapy; and possibly revaccination programs are important prevention strategies to consider for infection mitigation. Assessment of risk factors, evaluation, and treatment for pathogen(s) prevalent in a particular time frame post-CAR-T are important clinical considerations in patients presenting with clinical features suggestive of infectious pathology. As more data emerge on the topic, personalized risk assessments to inform the type and duration of prophylaxis use and planning interventions will continue to emerge. Herein, we review our current approach toward infection mitigation while recognizing that this continues to evolve and that there are differences among practices stemming from data availability limitations.

Abstract 4120657: A Personal Risk Assessment Device in Patients with Chest Pain

Introduction: Early prehospital chest pain assessment improves acute coronary syndrome (ACS) outcomes and can reduce unnecessary Emergency Room (ER) visits but requires medical personnel and electrocardiographic (ECG) equipment. Objective: To evaluate the feasibility of a personal credit-card sized chest pain self-assessment device (HeartBeam, Santa Clara, CA, USA) in diagnosing ACS. Methods: ACS risk was estimated by serial likelihood ratio analysis of 3 components: pre-existing coronary heart disease (CHD) risk (pre-calculated based on pooled ASCVD equation), chest pain characteristics (interactive proprietary questionnaire), and integrated near-orthogonal 3-lead self-recorded vectorcardiogram (VCG) with optional comparison to baseline using portable handheld HeartBeam recorder. Final ACS risk was graded High/Intermediate/Low by the cloud-based proprietary algorithm. High risk was considered positive for ACS. Results: 184 ER patients with chest pain (Age 57 +/- 7years, 90 Male, 43 (23%) ACS) comprised learning (n=96) and test (n=88) sets. ACS sensitivity was 28/28 (100%) in learning and 15/16 (94%) in test set with specificity of 43% and 42% respectively. In the subset of patients with pain-free portable VCG recorded 9 months later used as a “reverse baseline” (n=110) sensitivity was 17/17 (100%) and 11/12 (92%), specificity 54% and 58% in learning and test set, respectively (p&gt;0.1 with no baseline). Single patient missed by the analysis had known coronary disease with usual anginal episode resulting in minor troponin leak. In “reverse baseline” analysis 41/110 (37%) patients were classified as Low risk compared to 18/184 (10%) without baseline (p&lt;0.001), all negative for ACS. Based on the analysis of CHD risk factors, chest pain characteristics, and 12-lead ECG, a consensus of 3-member ER chest pain unit physicians panel had sensitivity/specificity of 94/54% for the whole and 93/48% for the “reverse baseline” groups (p&gt;0.1 with HeartBeam device). Conclusion: HeartBeam chest pain self-assessment device provides accurate ACS detection comparable to expert ER physicians using 12 lead ECG. The presence of baseline recording reduces the number of false positive results. If confirmed in larger studies, it can be used to facilitate outpatient chest pain assessment and triage by remote physicians and patients themselves.

Wildfire Smoke: Health Effects, Mechanisms, and Mitigation.

Wildfires are becoming more frequent and intense on a global scale, raising concerns about their acute and long-term effects on human health. We conducted a systematic review of the current epidemiological evidence on wildfire health risks and a meta-analysis to investigate the association between wildfire smoke exposure and various health outcomes. We discovered that wildfire smoke increases the risk of premature deaths and respiratory morbidity in the general population. Meta-analysis of cause-specific mortality and morbidity revealed that wildfire smoke had the strongest associations with cardiovascular mortality (RR: 1.018, 95% CI: 1.014-1.021), asthma hospitalization (RR: 1.054, 95% CI: 1.026-1.082), and asthma emergency department visits (RR: 1.117, 95% CI: 1.035-1.204) in the general population. Subgroup analyses of age found that adults and elderly adults were more susceptible to the cardiopulmonary effects of wildfire smoke. Next, we systematically addressed the toxicological mechanisms of wildfire smoke, including direct toxicity, oxidative stress, inflammatory reactions, immune dysregulation, genotoxicity and mutations, skin allergies, inflammation, and others. We discuss wildfire smoke risk mitigation strategies including public health interventions, regulatory measures, and personal actions. We conclude by highlighting current research limitations and future directions for wildfire research, such as elucidating the complex interactions of wildfire smoke components on human health, developing personalized risk assessment tools, and improving resilience and adaptation strategies to mitigate the health effects of wildfires in changing climate.

"Just Like, Risking Your Life Here": Participatory Design of User Interactions with Risk Detection AI to Prevent Online-to-Offline Harm Through Dating Apps

Social computing platforms facilitate interpersonal harms that manifest across online and physical realms such as sexual violence between online daters and sexual grooming through social media. Risk detection AI has emerged as an approach to preventing such harms, however a myopic focus on computational performance has been criticized in HCI literature for failing to consider how users should interact with risk detection AI to stay safe. In this paper we report an interview study with woman-identifying online daters (n=20) about how they envision interacting with risk detection AI and how risk detection models can be designed pursuant to such interactions. In accordance with this goal, we engaged women in risk detection model building exercises to build their own risk detection models. Findings show that women anticipate interacting with risk detection AI to augment - not replace - their personal risk assessment strategies. They likewise designed risk detection models to amplify their subjective and admittedly biased indicators of risk. Design implications involve the notion of personalizable risk detection models, but also ethical concerns around perpetuating problematic stereotypes associated with risk.

A Digital Self-Administered Check-Up Assessment and Evaluation of Risk Communication in Older People.

Digital health tools can significantly contribute to the continuum of care and support for persons with their self-management role. Despite the increasing adoption of digital health tools, little is known about the uptake and comfort of use among older persons. A mixed methods design was used to assess the feasibility and utility of the check-up (CU), a self-administered digital health assessment for older persons. For the older person, qualitative interviews and de-identified CU assessment data were collected. A focus group was held to gain the health provider perspective. A total of 32 online CU assessments were completed, and 17 individuals participated in interviews. Three main themes were identified: digital literacy and accessibility, communication of risk and role within the circle of care. Self-administered digital assessments are feasible for older adults. Strategies for adoption should focus on accessibility and collaboration with health professionals to maximise utility and understanding of assessment results.

Effective EFL University Teachers: Measuring Students’ Perceptions

Investigating perceptions of an effective university English as a foreign language (EFL) teacher has been an ongoing topic in the last few years. However, those studies have focused only on teachers’ perceptions, not students’ perceptions, from the Latin American context and mainly based on Likert scales questionnaires. This study aims to determine university students’ perceptions of what they distinguish as effective EFL university teachers. Based on the CEMEDEPU questionnaire, a new instrument called Effective EFL University Teacher (EEFLUT) of 71 items was created and applied to students from an Ecuadorian public university (N=716). A Confirmatory Factor Analysis was conducted using AMOS 21 software to evaluate three models. The third model was chosen as the most accurate amid its great performance. This model has three main dimensions (Personal Qualities, Teaching-Learning Methodology, and Assessment Methodology). The reliability of the instrument was evaluated with the KR-20, which showed that two out of three dimensions presented high reliability of &gt;.900. The findings demonstrate that the students value Personal Qualities (PQ) as the most relevant feature of an effective EFL university teacher above the Teaching-Learning Methodology and Assessment Methodology. These results reflect the importance of the participants attribute to the teacher’s personal qualities as one of the components of their satisfaction in learning language.

Exploring the Impact of Blood Draws on the Intraocular Pressure of Older Adults: A Focus on Physiological Responses.

Background/Objectives: This study investigated intraocular pressure (IOP) changes after a blood draw in older adults considering sex, age, and baseline IOP. Methods: Fifty-three subjects (54.7% females; age: 68.50 ± 4.46 years; Visual Function Index [VF14]: 94.50 ± 7.50 points; mean contrast sensitivity function (CSF) for both eyes in each spatial frequency [cdp]: 1.5 cdp [1.41 ± 0.20 log], 3 cdp [1.57 ± 0.29 log], 6 cdp [1.45 ± 0.39 log], 12 cdp [1.04 ± 0.40 log], 18 cdp [0.63 ± 0.31 log]) voluntarily participated. Subjects fasted for at least 8 h before attending the laboratory. First, IOP was measured in a seated position using a portable rebound tonometer. Ten minutes after the initial measurement, two 10 mL tubes of blood were drawn. Five minutes after the blood draw IOP was measured again following the same procedure as the initial measurement. We evaluated the differences using an analysis of variance. Results: Significant, but not clinically relevant, decreases were found in the right eye, with small effect sizes (p = 0.013-0.079, d = 0.35). Only males and subjects older than 68 years showed trends toward IOP reduction in the right eye. Subjects with baseline IOP ≥ 14 mmHg experienced significant IOP reductions in both eyes, with moderate effect sizes (p = 0.001-0.002, d = 0.56-0.69). Conclusions: Our findings suggest that a blood draw of 20 mL is safe for the IOP levels of older adults with baseline IOP between 11 and 21 mmHg. Variations in IOP were observed based on baseline IOP, sex, and age, suggesting the importance of personalized clinical assessments. The primary factor influencing IOP changes appears to be the baseline IOP level.

Impact of AI on Investment Decision and Financial Inclusion of Rural India: A Study on Young Rural Women Near Ranchi District

Abstract: The integration of Artificial Intelligence (AI) into the financial sector has revolutionized investment decision-making, offering a powerful tool to empower individuals in making informed choices about their financial future. This research paper explores the impact of AI on the investment decisions of rural consumers, shedding light on the transformative potential of AI in promoting financial inclusion in underserved regions. Rural consumers in emerging economies face unique challenges in accessing financial services and making sound investment decisions. The study aims to investigate how AI technologies, including machine learning algorithms and predictive analytics, can address these challenges by providing personalized investment advice, risk assessments, and portfolio optimization for rural consumers. It examines the key factors that influence the adoption and acceptance of AI-driven investment solutions among rural populations. Through a mixed-method research approach, including surveys, interviews, and data analysis, this paper analyses the application of AI in rural investment decision-making. It discusses the benefits of AI, such as improved risk management, increased financial literacy, and enhanced access to financial markets. Additionally, the research assesses potential concerns, including data privacy, ethical considerations, and the need for human oversight in AI-driven investment processes. The findings suggest that AI can bridge the financial literacy gap in rural areas and enhance the investment decision-making capabilities of rural consumers. However, this transformation requires a holistic approach, encompassing not only the development and deployment of AI technology but also regulatory frameworks and educational initiatives to ensure its responsible and equitable use. In conclusion, this research paper highlights the pivotal role of AI in democratizing access to financial opportunities for rural consumers, ultimately contributing to greater financial inclusion and economic growth in rural regions. It offers valuable insights for policymakers, financial institutions, and technology developers aiming to leverage AI to improve investment decisions and financial well-being for rural populations.

FUNCTIONAL ASSESSMENT METHODS FOR SPINAL CORD INJURY PATIENTS IN REHABILITATION PROCESS – A SYSTEMATIC REVIEW

Background: Spinal cord injury (SCI) leads to neurological damage that causes motor, sensory and autonomic neurologic functions decrease and/or loss. Evaluating the functionality of persons with SCI, especially in cases of incomplete injury, requires professional experience and skill. Objectives: This study aimed to gain a systematic overview of the functional assessment methods for SCI patients in the rehabilitation process. Search Methods: Systematic electronic searches were carried out using different databases from which 3489 registers were retrieved. Selection criteria: Studies that used tools to evaluate patients with SCI motor function as the manual test, Graded and Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP), ASIA Impairment Scale (AIS), Neuromuscular Recovery Scale (NRS), Walking Index SCI (WISCI), Walking Index SCI II (WISCI-II), functional independence measure (FIM) or Spinal Cord Independence Measurement III (SCIM-III). Reviews, animal studies, articles with more than ten years, theses and dissertations and/or out of the object of study were excluded. Data collection and analysis: Two experienced professionals searched the databases. All titles were read for the first selection. Afterward, the abstracts were read, and another number of articles were excluded. In the end, ten articles met the inclusion criteria of this study. Main results: We identified different tools used for the functional assessment of SCI persons, but 9 out of 10 studies did not detail the results by segment, and none presented a manual test for trunk evaluation. Robotic therapy combined with conventional therapy brings good results. Final Considerations: Ten studies that met inclusion criteria were identified. It was noticed that more in-depth studies are needed detailing the tests by segment, especially on trunk functionality in people with different levels of SCI.

Development and internal validation of prediction model for rebleeding within one year after endoscopic treatment of cirrhotic varices: consideration from organ-based CT radiomics signature

BackgroundRebleeding after endoscopic treatment for esophagogastric varices (EGVs) in cirrhotic patients remains a significant clinical challenge, with high mortality rates and limited predictive tools. Current methods, relying on clinical indicators, often lack precision and fail to provide personalized risk assessments. This study aims to develop and validate a novel, non-invasive prediction model based on CT radiomics to predict rebleeding risk within one year of treatment, integrating radiomic features from key organs and clinical data.Methods123 patients were enrolled and divided into rebleeding (n = 44) and non-bleeding group (n = 79) within 1 year after endoscopic treatment of EGVs. The liver, spleen, and the lower part of the esophagus were segmented and the extracted radiomics features were selected to construct liver/spleen/esophagus radiomics signatures based on logistic regression. Clinic-radiomics combined models and multi-organ combined radiomics models were constructed based on independent model scores using logistic regression. The model performance was evaluated by ROC analysis, calibration and decision curves. The continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices were analyzed.ResultsThe clinical-liver combined model had the highest AUC of 0.931 (95% CI: 0.887–0.974), which was followed by the liver-based model with AUC of 0.891 (95% CI: 0.835–0.74). The decision curves also showed that the clinical-liver combined model afforded a greater net benefit compared to other models within the threshold probability of 0.45 to 0.80. Significant improvements in discrimination (IDI, P < 0.05) and reclassification (NRI, P < 0.05) were obtained for clinical-liver combined model compared with the independent ones.ConclusionThe independent and combined liver-based CT radiomics models performed well in predicting rebleeding within 1 year after endoscopic treatment of EGVs.

Credit Anomaly Detection Method based on Bayesian Networks

Due to the virtual nature of online transactions and the underdeveloped personal credit risk assessment system of Internet finance lending platforms in China, these platforms often struggle to verify the creditworthiness of applicants prone to fraud and default, leading to loan losses. As a result, risk control has become the core of Internet lending platforms. Modern risk control relies on statistical and data mining techniques to analyze and model data, uncover patterns that indicate loan defaults, and identify anomalies. This paper proposes a Bayesian network-based credit anomaly detection method, which detects anomalies by calculating and ranking the joint anomaly probabilities of sample instances. The technique operates under unsupervised learning and can effectively handle missing and imbalanced data. This method analyzes a loan credit dataset with 32,581 samples and 12 features of the customers. The result of the analysis demonstrates the feasibility and effectiveness of this method in detecting anomalies in online loans.

Unsupervised machine learning identifies distinct phenotypes in cardiac complications of pediatric patients treated with anthracyclines

BackgroundAnthracyclines are essential in pediatric cancer treatment, but patients are at risk cancer therapy-related cardiac dysfunction (CTRCD). Standardized definitions by the International Cardio-Oncology Society (IC-OS) aim to enhance precision in risk assessment.ObjectivesCategorize distinct phenotypes among pediatric patients undergoing anthracycline chemotherapy using unsupervised machine learning.MethodsPediatric cancer patients undergoing anthracycline chemotherapy at our institution were retrospectively included. Clinical and echocardiographic data at baseline, along with follow-up data, were collected from patient records. Unsupervised machine learning was performed, involving dimensionality reduction using principal component analysis and K-means clustering to identify different phenotypic clusters. Identified phenogroups were analyzed for associations with CTRCD, defined following contemporary IC-OS definitions, and hypertensive response.ResultsA total of 187 patients (63.1% male, median age 15.5 years [10.4–18.7]) were included and received anthracycline chemotherapy with a median treatment duration of 0.66 years [0.35–1.92]. Median follow-up duration was 2.78 years [1.31–4.21]. Four phenogroups were identified with following distribution: Cluster 0 (32.6%, n = 61), Cluster 1 (13.9%, n = 26), Cluster 2 (24.6%, n = 46), and Cluster 3 (28.9%, n = 54). Cluster 0 showed the highest risk of moderate CTRCD (HR: 3.10 [95% CI: 1.18–8.16], P = 0.022) compared to other clusters. Cluster 3 demonstrated a protective effect against hypertensive response (HR: 0.30 [95% CI: 0.13– 0.67], P = 0.003) after excluding baseline hypertensive patients. Longitudinal assessments revealed differences in global longitudinal strain and systolic blood pressure among phenogroups.ConclusionsUnsupervised machine learning identified distinct phenogroups among pediatric cancer patients undergoing anthracycline chemotherapy, offering potential for personalized risk assessment.

Machine learning for pacemaker implantation prediction after TAVI using multimodal imaging data

Pacemaker implantation (PMI) after transcatheter aortic valve implantation (TAVI) is a common complication. While computed tomography (CT) scan data are known predictors of PMI, no machine learning (ML) model integrating CT with clinical, ECG, and transthoracic echocardiography (TTE) data has been proposed. This study investigates the contribution of ML methods to predict PMI after TAVI, with a focus on the role of CT imaging data. A retrospective analysis was conducted on a cohort of 520 patients who underwent TAVI. Recursive feature elimination with SHAP values was used to select key variables from clinical, ECG, TTE, and CT data. Six ML models, including Support Vector Machines (SVM), were trained using these selected variables. The model’s performance was evaluated using AUC-ROC, F1 score, and accuracy metrics. The PMI rate was 18.8%. The best-performing model achieved an AUC-ROC of 92.1% ± 4.7, an F1 score of 71.8% ± 9.9, and an accuracy of 87.9% ± 4.7 using 22 variables, 9 of which were CT-based. Membranous septum measurements and their dynamic variations were critical predictors. Our ML model provides robust PMI predictions, enabling personalized risk assessments. The model is implemented online for broad clinical use.

Changes in Auditory Performance Following a Virtual Reality Music Concert.

The purpose of this study was to evaluate threshold and suprathreshold auditory risk from a newly popular platform of music concert entertainment; virtual reality (VR) headsets. Recreational noise exposure to music is the primary source of hearing hazard in young-adults, with noise doses of in-person concert venues and music festivals well in excess of the recommended daily exposure recommendation from the National Institute for Occupational Safety and Health. While research on the relationship between personal music players and noise-induced hearing loss risk is abundant, no study has yet evaluated noise-induced hearing loss risk from VR headsets, which are newest to the commercial market at this time. Thirty-one young-adult participants (18 to 25 years) with normal-hearing sensitivity (0 to 16 dB HL) experienced a VR music concert and participated in three data collection timepoints: Session A preexposure, Session A post-exposure, and Session B post-exposure. Participants underwent baseline testing for audiometry (0.25 to 20 kHz), distortion product otoacoustic emission testing (1 to 10 kHz), and Words-in-Noise testing. Participants then wore a commercially available VR headset (Meta Quest 2) and experienced a freely available online VR music concert (via the video-sharing website "YouTube"). The VR music concert duration was 90 min set to maximum volume, which yielded an average sound level equivalent of 78.7 dBA, max sound level of 88.2 dBA, and LC peak sound level of 98.6 dBA. Post-exposure testing was conducted immediately at the conclusion of the VR concert, and again within 24 hr to 1 week after the exposure. Participants also answered a questionnaire that estimated noise exposure history (National Acoustics Laboratory "Noise Calculator"). Post-exposure deficit was not observed in DPOAEs or Words-in-Noise score (p's > 0.05). However, statistically significant temporary post-exposure deficit was observed in audiometry at 4, 8, and 12.5 kHz (p's < 0.05) (mean differences: 2 to 3 dB HL). Twenty-four hours and 1-week post-exposure measurements revealed no permanent changes from baseline measurements (p's > 0.05) aside from one spurious difference at 12.5 kHz. Males tended to exhibit a significantly higher noise history score on average than females. The primary, secondary, and tertiary sources of noise hazard history in this young-adult cohort included amplified music. These preliminary data suggest that VR music concerts-which are likely to produce a substantially lower noise dose than in-person music concerts-may still be capable of producing at least slight, temporary threshold shifts on the order of 2 to 3 dB HL. Future research should include VR headsets in personal music player risk assessment, as the VR music concert platform is increasing rapidly in popularity among young-adults.

Disease Prediction and Drug Recommendation Using ML

In today's dynamic healthcare landscape, the integration of technology has become indispensable. The project sets out to create a cutting-edge healthcare solution that will transform the very foundations of healthcare decision-making by utilizing the powerful powers of machine learning. The main objective of this project is to develop a dual-dashboard system that will serve both patients and medical staff. The main objective is to reduce the gap between patients and healthcare professionals, which has existed for a long time. It makes it possible for people to receive highly personalized health assessments, enables efficient appointment scheduling, and provides doctors with recommendations for data-driven treatments. Ultimately, this project aims to profoundly impact the accessibility and quality of healthcare services. It seeks to enhance the patient experience, augment the precision of medical decision-making, and foster seamless collaboration between individuals and their healthcare providers.

A Tribute to Jaume Porta Casanellas and His Influence on Soil Science

This article provides personal and professional assessments from disciples, colleagues and friends of Jaume Porta Casanellas (Barcelona, 1944; Lleida, 2023), a prominent soil scientist. He began his agricultural engineering studies at the Polytechnic University of Madrid (UPM) where he met Marta López-Acevedo, his wife and outstanding collaborator. At UPM, he started his early work in soil science under the guidance of Professor Carlos Roquero who became his mentor and friend. Jaume Porta was a dedicated, passionate soil scientist who engaged extensively in teaching and research in Soil Science, while also excelling as a manager. He emerged as a leader due to his initiatives in promoting Soil Science in Catalonia and Spain, and for his forward-thinking vision, evident in his decisions as Rector of the University of Lleida, which have significantly contributed to the city’s development. From the beginning, he advocated for detailed (1:25,000) soil mapping of Catalonia to enhance territorial planning and agricultural progress. His primary research focus was on salt-affected soils and soils with gypsum, alongside soil erosion and conservation. Porta devoted a lot of effort to improve soil field descriptions with his Agenda de campo. He played a key role in standardizing soil analytical methods, establishing large series laboratories in Spain, notably the LAF in Sidamon (Lleida), and aligning Spanish soil labs with the international GLOSOLAN network. As president of the SECS, he energized activities and encouraged member participation. His educational publications, mainly his comprehensive textbook Edafología, are considered fundamental in Soil Science across Spanish-speaking countries, as is the Multilingual Dictionary of Soil Science, representing the pinnacle of his efforts to rigorously disseminate soil science concepts and terms in Spanish, Catalan, Galician, and Portuguese. He contributed significantly to international Soil Science courses in Mexico and played a key role in establishing the JADE postgraduate training program. He facilitated the creation and international visibility of the Spanish Journal of Soil Science. Additionally, he advocated for the establishment of the Soil Sciences Documentation Centre (Ce.SECS) to preserve historical publications and the legacy of soil scientists. Jaume Porta’s enduring impact, both professionally and personally, will be felt for years to come.

Enhancing Flexible Assessment Through ePortfolios: A Scholarly Examination

In contemporary educational settings, the demand for flexible assessment practices has gained prominence due to students' diverse learning needs and preferences. Portfolios have emerged as a versatile tool for facilitating personalised and adaptable assessment processes. This paper investigates how ePortfolios can cater to students' flexible assessment needs. By examining the benefits, challenges, and implementation strategies associated with ePortfolios, this paper highlights their potential to enhance assessment practices, foster student engagement, and promote lifelong learning. As part of implementation strategies, we provide examples to support teaching staff and institutions in translating the underpinning principles and theory into actionable practice.

Overall survival and disease-free survival prediction in Chinese women with breast cancer aged 70 years or older by using nomograms.

By analyzing the existing data of this study, a prediction tool for the overall breast cancer survival and disease-free survival (DFS) of elderly women was established. Clinicopathologic data were collected from elderly women with BC who were admitted to the Tianjin Medical University Cancer Institute and Hospital from August 2014 to December 2017. Independent prognostic factors for BC in elderly patients were confirmed using the Cox proportional hazards model. Nomograms were developed with these factors for predicting the 3- and 5-year overall survival (OS) as well as DFS. The nomograms' discrimination ability and calibration were assessed through the area under the curve (AUC), concordance index (C-index), decision curve analysis (DCA), and calibration plots. We enroled 889 elderly patients with BC, and the results showed that the 3-year OS rate was 93.4% (95%CI = 91.8%-95.1%), the 3-year DFS rate was 87.8% (95%CI = 85.7%-90.0%), the 5-year OS rate was 85.6% (95%CI = 83.3%-87.9%), and the 5-year DFS rate was 80.1%(95%CI = 77.5%-82.8%). The corrected C-indices of the OS and DFS nomograms were 0.799 and 0.667, respectively (95%CI = 0.767-0.830 and 0.632-0.702, respectively). Relatively high AUC values were shown by the nomograms for estimating OS and DFS. The DCA revealed that the constructed nomograms had net benefits for clinical application. The calibration curves demonstrated an excellent correspondence between the data predicted by the nomograms and the actual survival data. Survival curves indicated that risk stratification could differentiate OS and DFS. This study developed novel and practical nomograms for individual prediction of DFS and OS in elderly BC patients. These nomograms can predict 3- and 5-year OS as well as DFS in the elderly BC patient population, thereby enabling personalized risk assessment and risk-based therapy.