Search Results (649)

Background: Numerous healthcare services have been affected by the COVID-19 pandemic worldwide. Specialized healthcare services were postponed or canceled, potentially compromising regular services for hemato-oncology patients. The current study aimed to analyze the impact of the COVID-19 pandemic on access to hemato-oncology services in Almaty, the largest city in Kazakhstan. Methods: We retrospectively analyzed the socio-demographic characteristics of patients admitted to two large tertiary centers rendering hemato-oncology services, the City Clinical Hospital 7 (H7) and the Kazakh Institute of Oncology and Radiology (KazIOR). All data were retrieved for the period spanning from 1 March 2019 to 28 February 2022. The retrieved variables included age, gender, type of residence, hospitalization rate, treatment outcomes (discharged/deceased), bed days, diagnoses according to International Classification of Diseases (ICD-10) (acute leukemia and hematopoietic depression, lymphoproliferative diseases, and myeloproliferative diseases), and referral sources (ambulance, another hospital, consultative and diagnostic assistance, primary healthcare, self-referral, and referrals from hematologists’ offices). Results: In the 2019–2022 period, 6763 hemato-oncology hospitalizations were registered: 3583 in H7 and 3180 in KazIOR. The mean age at hospitalization was 55.04 (SD = 16.07) for females and 51.2 (SD = 16.7) for males. The proportion of hospitalized urban and rural patients differed significantly: 6191 (92%) and 571 (8,4%), respectively (χ² = 13.8, p = 0.001). In the 2020–2021 period, fewer patients were discharged (n = 2047) compared to 2019–2020 (n = 2387) and 2021–2022 (n = 2081) (χ² = 20.09, p = 0.003). However, the proportion of deaths in the 2020–2021 period (3.5%) was higher than in the 2019–2020 (3.2%) and 2021–2022 periods (2.6%) (χ² = 20.09, p = 0.003). A total of 403 (19%) hospital admissions were carried out by ambulance (emergency cases) in the 2020–2021 period, 368 (14.8%) in 2019–2020, and 394 (18.3%) in 2021–2022 (χ² = 2231, p < 0.001). The number of patients transferred from other hospitals to H7 and KazIOR increased by 12.4% in the 2020–2021 period. Conclusions: Our findings indicate a negative impact of the COVID-19 pandemic on access to hemato-oncology services, leading to increased mortality. Further studies are warranted to explore factors underlying the trends in hospitalizations and mortality of hemato-oncology patients during healthcare crises. Full article

Background: Early and accurate differentiation between melanomas and benign nevi is essential for making proper clinical decisions. This study aimed to identify clinical, morphological, and histopathological variables most strongly associated with melanoma, using both statistical and machine learning approaches. Methods: This study evaluated 184 melanocytic lesions using clinical, morphological, and histopathological parameters. Univariable analyses were performed in XLStat statistical software, version 2014.5.03, while multivariable machine learning models were developed in Jamovi (version 2.4). Five supervised algorithms (random forest, partial least squares, elastic net regression, conditional inference trees, and k-nearest neighbors) were compared using repeated cross-validation, with performance evaluated by accuracy, Kappa, sensitivity, specificity, F1 score, and calibration. Results: Univariable analysis identified significant differences between melanomas and nevi in age, horizontal diameter, gender, lesion location, and selected histopathological features (cytological and extracellular matrix changes, epidermal interactions). However, several associations weakened in multivariable analysis due to collinearity and overlapping effects. Using glmnet, the most influential independent predictors were cytological changes, horizontal diameter, epidermal interactions, and extracellular matrix features, alongside age, gender, and lesion location. The model achieved high discrimination (AUC = 0.97, 95% CI: 0.93–0.99) and accuracy (training: 95.3%; test: 92.6%), confirming robustness. Conclusions: Structured demographic, morphological, and histopathological data—particularly age, lesion size, cytological and extracellular matrix changes, and epidermal interactions—can effectively support classification of melanocytic lesions. Machine learning approaches (the glmnet model in our study) provide a reliable framework to evaluate such predictors and offer practical diagnostic support in dermatopathology. Full article

Background/Objectives: Alcohol consumption can increase the risk of dyslipidemia, thereby elevating the risk of cardiovascular diseases. However, the relationship between alcohol consumption patterns and dyslipidemia remains controversial. Based on large-scale cross-sectional data from the Chinese population, this study aims to investigate the correlations between various alcohol consumption behaviors and dyslipidemia among adult residents in China. Methods: Our analysis utilized data from the 2015–2017 China Nutrition and Health Surveillance project, which provides a large, nationally representative sample (N = 52,471). We employed a binary logistic regression model specifically designed for complex sampling frameworks. This model was utilized to assess the relationship between various alcohol consumption behaviors (including daily alcohol intake levels and drinking frequency) and the incidence of hypercholesterolemia, hypertriglyceridemia, low levels of high-density lipoprotein cholesterol (low HDL-C), and elevated levels of low-density lipoprotein cholesterol (high LDL-C). Drinking behaviors were classified into three distinct categories for analysis: China classification (never, moderate, excessive), WHO classification (never, low-risk, medium-risk, high-risk), and drinking frequency (never, <1, 1–3, 4–6, ≥7 times/week). Results: Compared with never drinkers, the risk of hypercholesterolemia was significantly higher in men who were excessive drinkers (aOR = 1.39, 95%CI: 1.24–1.57), medium-risk drinkers (aOR = 1.24, 95%CI 1.01–1.53), high-risk drinkers (aOR = 1.67, 95%CI: 1.4–1.95), and those who drank more than once a week (aOR range: 1.27–1.65), and there was no such association in women (p > 0.05). Compared with never drinkers, the risk of hypertriglyceridemia was higher in male drinkers with excessive drinking (aOR = 1.35, 95%CI: 1.24–1.47), medium-risk drinking (aOR = 1.29, 95%: 1.11–1.50), high-risk drinking (aOR = 1.52, 95%CI: 1.3–1.71), and a drinking frequency more than 1 time/week (aOR range: 1.22–1.38), while in women, it was moderate drinking (aOR = 0.85, 95%CI 0.77–0.94), low-risk drinking (aOR = 0.86, 95%CI 0.78–0.94), and a drinking frequency of more than once a week (aOR = 0.74, 95%CI 0.63–0.87) that reduced the occurrence of hypertriglyceridemia. Compared with non-drinkers, men with any drinking status had a lower risk of low HDL-C (aOR range: 0.38–0.90) and a similar association was also observed in women (aOR range: 0.26–0.84). Compared with never drinkers, male excessive drinkers (aOR = 0.86, 95%CI: 0.77–0.97), medium-risk drinkers (aOR = 0.80, 95%CI:0.65–0.99), high-risk drinkers (aOR = 0.83, 95%CI: 0.70–0.97), and those with a drinking frequency of 1–3 times/week (aOR = 0.89, 95%: 0.79–0.99) had a lower risk of high LDL-C, and there was no such association in women (p > 0.05). Conclusions: Significant gender differences were observed in the effects of alcohol consumption on lipid profiles. Men who were excessive drinkers, medium-risk drinkers, high-risk drinkers, and those who drank more than once a week had a higher risk of hypercholesterolemia and hypertriglyceridemia, but a lower risk of low HDL-C and high LDL-C. In women, moderate drinking was associated with a reduced risk of hypertriglyceridemia. Any alcohol consumption and drinking frequency more than 1 time/week were associated with a lower risk of low HDL-C in women. No significant association was found between alcohol consumption and hypercholesterolemia or high LDL-C in women. Full article

Poetry reading remains a largely underexplored area in phonetic research. While previous studies have highlighted its potential and challenges, experimental research in the Spanish context is still limited. This study aims to examine the evolution of Spanish poetry reading over time, focusing on its main prosodic features. Applying the VIP-VSP phonetic model to 40 poetry recordings, we analyzed the organizational and prosodic indices that characterize poetry reading. Mean speech rate, plenus (the ratio of speaking time to pausing), and pitch span emerged as key parameters for capturing change. The results identified two distinct historical phases—first and second radio-television—showing significant effects on speech rate, plenus, and pitch span: speech rate and pitch span increased over time, while plenus decreased. Gender also played a key role, with female voices exhibiting significantly higher values in both pitch span and plenus. Variability and recurring strategies were observed within and across authors. This study confirms that poetry reading has evolved along a ‘stylistic-chronological’ trajectory, while also reflecting gender-based distinctions. These findings underscore the need for interdisciplinary analytical approaches and diversified classification groupings to fully capture the complexity of this mode of speech. Full article

Leptospirosis is a zoonotic disease caused by Leptospira spp., with over 250 serovars classified into 24 serogroups. Control measures depend on understanding serovar-specific epidemiology, yet the microscopic agglutination test (MAT) is only serogroup specific, and classification is complicated by cross-reactivity. While MAT is the reference standard for leptospirosis serodiagnosis and seroepidemiological studies, factors influencing serogroup cross-reactivity remain poorly characterized. We investigated the relationship between age, antibody titer, and serogroup diversity among seropositive individuals in a population-based serosurvey in the Dominican Republic. Between June and October 2021, we conducted a multistage national serosurvey, enrolling 6683 participants across 134 clusters. MAT was performed on sera from 2091 participants in two provinces using a 20-serovar panel. MAT positivity was defined as titers ≥ 1:100. Generalized Additive Models were used to assess associations between age, maximum titer, and serogroup diversity. Of 2091 tested samples, 237 (11.3%) were seropositive. Older individuals and those with higher titers reacted to a greater number of serogroups (p = 0.005 and p < 0.0001, respectively). However, mean maximum titer decreased with age, suggesting that broader serogroup reactivity in older individuals reflects cumulative exposure rather than higher titers. Maximum titer was the strongest predictor of serogroup breadth, while gender, study region, and urban/rural setting were not significant. Overall, our findings demonstrate that serogroup cross-reactivity in MAT was significantly influenced by antibody titer and prior exposure, with older individuals displaying broader serogroup reactivity despite lower titers. These findings highlight key considerations for interpreting MAT results in seroepidemiological studies and underscore the limitations of MAT in serogroup-level classification. Full article

Accurate gender identification from written text is critical for author profiling, recommendation systems, and demographic analytics in digital ecosystems. This study introduces a scalable framework for gender classification in Turkish, combining contextualized BERTurk and subword-aware FastText embeddings with three meta-heuristic feature selection algorithms: Genetic Algorithm (GA), Jaya and Artificial Rabbit Optimization (ARO). Evaluated on the IAG-TNKU corpus of 43,292 balanced Turkish news articles, the best-performing model—BERTurk+GA+LSTM—achieves 89.7% accuracy, while ARO reduces feature dimensionality by 90% with minimal performance loss. Beyond in-domain results, exploratory zero-shot and few-shot adaptation experiments on Turkish e-commerce product reviews demonstrate the framework’s transferability: while zero-shot performance dropped to 59.8%, few-shot adaptation with only 200–400 labeled samples raised accuracy to 69.6–72.3%. These findings highlight both the limitations of training exclusively on news articles and the practical feasibility of adapting the framework to consumer-generated content with minimal supervision. In addition to technical outcomes, we critically examine ethical considerations in gender inference, including fairness, representation, and the binary nature of current datasets. This work contributes a reproducible and linguistically informed baseline for gender profiling in morphologically rich, low-resource languages, with demonstrated potential for adaptation across domains such as social media and e-commerce personalization. Full article

Background: Leukemia, a hematologic malignancy, is the major fluid tumor. However, there is a paucity in laboratory characterization in South Africa due to limited diagnostic infrastructure. Chromosomal aberrations play a crucial role in leukemia pathogenesis, influencing classification, prognosis, and treatment. Aim: This study aimed to characterize chromosomal aberrations in leukemia patients using the fluorescence in situ hybridization (FISH) method, with the goal of improving diagnostic precision and guiding tailored treatment in resource-limited settings. Methodology: This study was a retrospective analysis of 349 leukemia patient records from the NHLS Corporate Data Warehouse, covering cases diagnosed between January 2019 and January 2024. Chromosomal aberrations were assessed using FISH, including cases of CML, AML, CLL, and ALL. Results: CML was the most prevalent leukemia subtype (40%), followed by AML (31%). Age-specific distributions were significant across subtypes (p < 0.0001). FISH detected subtype-specific aberrations: t(1;19) and t(12;21) in 25% of ALL cases; t(8;21) and t(15;17) in 22–33% of AML cases; and t(9;22) in 100% of CML cases. In CLL, 13q deletions were most common (53% complex, 33% simple). Conclusions: This study reveals distinct chromosomal aberration patterns in leukemia patients in Gauteng, with CML as the most prevalent subtype. Distinct patterns were observed across ALL, AML, and CLL, with age and gender-specific trends. Findings highlight regional genetic influences, diagnostic gaps, and healthcare challenges, emphasizing the urgent need to expand cytogenetic and molecular testing to enable targeted diagnostics, risk stratification, and personalized therapies in sub-Saharan Africa. Full article

Background: Seafarers are highly susceptible to work-related injuries, which can result in serious consequences or permanent disabilities. Understanding the frequency and characteristics of occupational injuries is crucial for developing effective prevention strategies and identifying their underlying patterns and causes. This study aimed to determine the frequency and characteristics of telemedicine-assisted work-related injuries among seafarers on board Italian-flagged vessels. Methods: A retrospective descriptive study was conducted to analyze occupational injuries using medical data recorded in the Centro Internazionale Radio Medico (C.I.R.M.) database from 1 January 2010 to 31 December 2022. Injuries in the database were coded according to the 10th revision of the International Classification of Diseases (ICD-10) by the World Health Organization (WHO). Variables extracted from the database included injury type, seafarers’ age, rank, nationality, worksite, gender, date of injury, affected body region, clinical outcomes, and other demographic and occupational characteristics. Injury frequency and characteristics (e.g., location, type, and cause) were analyzed and stratified by seafarers’ rank and worksite groups. Results: The analysis included 793 seafarers who sustained injuries. Their average age was 39.15 ± 10.49 years (range: 21 to 70 years). Deck ratings and engine officers accounted for 27.9% and 20% of those who claimed injuries, respectively. 39.2% of injured seafarers were aged between 30 and 40 years. In terms of affected body parts, the most reported injuries were to the hand/wrist (33.3%), followed by the knee/lower legs (21%), and the head/eye (19%). Open wounds (38%) and burns/abrasions (14%) were the most common types of injury. Slips/falls (32%), burns/explosions (16.6%), and overexertion while lifting or carrying (14.8%) were the leading causes of injury during the study period. Nearly 35% of injuries affected workers on the deck and were due mainly to slips/falls, 19% in the engine room were due to being caught in machinery or equipment, and 32.5% in the catering department were due to burns/explosions. Conclusions: One-third of seafarers who suffered work-related injuries sustained hand and/or wrist injuries, with slips/falls being a significant cause. The results of this study emphasize the need for preventative measures in the marine sector, particularly to reduce risks associated with slips and falls, overexertion, and other injury-causing factors. Campaigns for the larger use of protective equipment are desirable to reduce occupational accidents at sea and provide better health protection for seafarers. Full article

Soft biometric prediction—including age, gender, and ethnicity—is critical in surveillance applications, yet often suffers from performance degradation as the subject-to-camera distance increases. This study hypothesizes that embedding distance-awareness into the training process can mitigate such degradation and enhance model generalization across varying visual conditions. We propose a distance-adaptive, multi-task deep learning framework built upon EfficientNetB3, augmented with task-specific heads and trained progressively across four distance intervals (4 m to 10 m). A weighted composite loss function is employed to balance classification and regression objectives. The model is evaluated on a hybrid dataset combining the Front-View Gait (FVG) and MMV annotated pedestrian datasets, totaling over 19,000 samples. Experimental results demonstrate that the framework achieves up to 95% gender classification accuracy at 4 m and retains 85% accuracy at 10 m. Ethnicity prediction maintains an accuracy above 65%, while age estimation achieves a mean absolute error (MAE) ranging from 1.1 to 1.5 years. These findings validate the model’s robustness across distances and its superiority over conventional static learning approaches. Despite challenges such as computational overhead and annotation demands, the proposed approach offers a scalable and real-time-capable solution for distance-resilient biometric systems. Full article

This study addresses the task of simultaneous age estimation and gender classification from facial images using convolutional neural networks (CNNs). The objective was to develop a unified model capable of handling both regression and classification tasks effectively. Four models with varying architectures, loss functions, and preprocessing strategies were implemented and evaluated. The best-performing model achieved over 90% accuracy in gender classification and a mean absolute error (MAE) below four years for age estimation. Performance analysis showed variation across age groups, with reduced accuracy for elderly individuals due to dataset imbalance and improved predictions for younger and middle-aged adults. To assess generalization, the model was also tested on external images, maintaining strong performance, particularly in gender classification. Challenges such as overfitting and face misdetection were addressed through preprocessing adjustments and model tuning. Beyond empirical results, this work consolidates a unified, reproducible protocol for joint age estimation and gender classification on a widely used face database. We standardize preprocessing, implement a consistent image-level split with a published seed, and define task-appropriate metrics. All training details are documented to provide a baseline, enhanced by a qualitative error analysis, enabling consistent reporting and facilitating future extensions. This approach demonstrates the effectiveness of CNNs for age and gender prediction and highlights their potential integration into recommendation systems that personalize content based on demographic attributes. Full article

Purpose: The accurate intraocular lens (IOL) power calculation is essential in phacoemulsification. The latest IOL power calculation formulas, and their new classification and method to assess their accuracy, were described and analyzed. Design: Narrative review. Methods: The manuscript includes articles on IOL power calculation published between 2019 and 2025. The following key words, such as “phacoemulsification”, “IOL power calculation formula”, “AI-based formulas”, “IOL power selection”, “IOL formulas classification”, IOL prediction” were used to identify papers by searching medical databases (Pubmed/MEDLINE, Google Scholar, Crossref). PRISMA methodology was used to select articles. Finally, 33 peer-reviewed English-language studies with a sample size of at least 120 eyes were included in the analysis. Results: Ten IOL power calculation formulas that have been introduced and published over the past 5 years were included in the study. Five of them are artificial intelligence based (Karmona, Hoffer QST, Nallasamy, Zhu-Lu and Zeiss-AI), four are vergence (Emmetropia Verifying Optical, Naeser 2, Voytsekhivskyy Regression Function-Gender and Castrop), and one is ray-tracing (the O formula). In this review, the formulas are introduced and analyzed, with a discussion of selected studies assessing the accuracy of these IOL power calculation methods. Conclusions: New IOL power calculation formulas are constantly being developed. They are mostly based on artificial intelligence. New methods are still being sought to assess the accuracy of formulas, and root mean square absolute error is one of them Full article

This paper examines the quotative system of Sri Lankan English. Quotatives are identified in face-to-face conversations in the Sri Lankan component of the International Corpus of English. The use of kiyala indicating and following quoted material has been transferred from Sinhala, one of the indigenous languages of the country, into Sri Lankan English. Together with the occurrence of complementising that, the localisation of the Sri Lankan English quotative system is evident. Special emphasis is given to the choice between BE like and SAY, the by far most frequent quotative forms in the informal spoken data analysed. They are annotated with established structural (e.g., content of the quote or tense) and sociobiographic variables (e.g., age and gender of the speaker) apparent from earlier quotative research, but also with new ones (e.g., quote length or speakers’ stays abroad or media exposure to particular varieties of English). Via a generalised linear mixed-effects model tree implementing the latest methodological suggestions for classification trees, it is found that BE like is favoured over SAY in Sri Lankan English with younger speakers—particularly when the conversation took place after 2015 and events are narrated using the historical present. Full article

Positional plagiocephaly is a common condition in infants, characterized by asymmetrical posterior occipital flattening due to external mechanical pressure. The Argenta classification is an observational diagnostic tool that classifies plagiocephaly into five degrees of severity according to specific cranial asymmetry characteristics. The purpose was to examine the inter-rater and intra-rater reliability of the Argenta classification among developmental physiotherapists. Two raters examined 42 infants aged 6 weeks to 12 months that were separately enrolled in Clalit child development centers. A second observation was held within 7 days of the first observation. A strong agreement was reached among the raters (κ = 0.85, p < 0.0001) and within two observations of the same rater (κ = 0.90, p < 0.0001). The high degree of agreement indicates the high reliability of the Argenta classification. No significant relationships between severity and gender, age, flattened side, primaparity, number of pregnancies, type of birth, or the Alberta Infant Motor Scale score were found. In conclusion, the Argenta classification demonstrates high inter- and intra-rater reliability, particularly for milder severity levels. It is an easy, quick, and free method to use clinically without causing inconvenience to the assessed infant. While these results support clinical integration, particularly in resource-limited settings, the variable reliability across severity levels indicate that further validation studies are needed before universal adoption. Full article

Background/Objectives: Suicidal risk is a major public health concern among adolescents. Pharmacological treatment in this population remains complex and often targets underlying psychiatric disorders rather than suicidal risk itself. This study aimed to examine associations between suicidal concern (SC) and psychotropic prescriptions in hospitalized adolescents with psychiatric disorders. A secondary aim was to assess whether suicidal risk level predicted pharmacological treatment at discharge (T1). Methods: A cross-sectional study was conducted on 224 adolescents (men age = 15.4, SD = 1.55). SC was assessed using the Columbia Suicide Severity Rating Scale (C-SSRS). Participants were categorized into SC and without SC (No Suicidal Concern—NSC) groups and further stratified by low and high suicidal risk. Psychotropic prescriptions at admission (T0) and T1 were compared, adjusting for age, gender, and psychiatric diagnoses. Results: The SC group showed more severe clinical presentations, including higher symptom burden and functional impairment. At discharge, they were more frequently prescribed antipsychotics and supplements, and more often received multiple medications compared to the NSC group. Logistic regression confirmed SC as a significant predictor of antipsychotic and supplement prescriptions at T1, independent of age, gender, and diagnosis. In contrast, suicidal risk level did not significantly predict specific prescriptions or polypharmacy prescriptions. Conclusions: SC appears to influence pharmacological decisions beyond diagnostic classifications, with a tendency toward risk-containment strategies. These findings emphasize the need for individualized, developmentally appropriate, and evidence-based treatment planning. Importantly, SC should be considered not only as a symptom but as a potential independent treatment target in adolescent psychiatry. Full article

Incorporating scientific and critical thinking in early childhood education is essential for developing individuals with strong thinking skills. This study assesses the impact of promoting scientific thinking on the development of critical thinking among fifth-graders in central Mexico. We hypothesize that involving students in scientific thinking tasks would further enhance essential thinking skills. The methodology involved longitudinal quasi-experimental design that included a pre-test and post-test, along with a comparison (control) group. Critical thinking was measured using a test that was applied at the start and end of our study to assess four thinking skills: classification, problem-solving, decision-making and logical reasoning. The control group was taught using a standardized methodology, while the experimental group was taught about the structure of the scientific method using a template with characters that guided each step: observation, question, hypothesis, experimentation and conclusions. Students from the critical and scientific thinking experimental group had higher scores on the post-assessment than those of the control group, suggesting that learning through scientific thinking tasks improved their critical thinking skills. Boys’ critical thinking scores were higher than girls’ in both groups, while scientific thinking scores were not associated with gender. We demonstrated that critical thinking improves when children incorporate scientific thinking into their learning abilities. Full article