Browse Articles

A recent update on the morphological classification of intraductal papillary neoplasm of the bile duct: Correlation with postoperative prognosis and pathological features

Purpose We proposed a novel morphological classification for intraductal papillary neoplasm of the bile duct (IPNB) and evaluated its association with postoperative prognosis. Methods Forty-two IPNB patients who underwent surgical resection were classified morphologically into three types—branched (n = 10), main duct (n = 26), and mixed (n = 6)—based on preoperative imaging features indicating cystic and/or bile duct involvement. Among them, 32 patients with evaluable specimens were further categorized pathologically into Type 1 (n = 10) and Type 2 (n = 22). Patient characteristics and postoperative outcomes were analyzed. Results Intraepithelial neoplasia was more frequently observed in the branched type, whereas invasive carcinoma predominated in the main duct type. In the mixed type, a half of patients involved both intra- and extrahepatic bile ducts, and this type also showed the highest incidence of residual tumor. The mixed type had the poorest 5-year postoperative survival rate (50%), compared to 90% in the main duct type and 100% in the branched type. It also exhibited the highest 5-year recurrence rate (62%). Among IPNB patients with associated invasive carcinoma, tumor infiltration beyond the bile duct wall (p < 0.001) and lymph node metastasis (p = 0.021) were significantly associated with poor prognosis, whereas the anatomical extent of the lesion (intrahepatic, extrahepatic, or both) was not. Morphological classification was significantly correlated with pathological subtypes: the branched type was predominant in Type 1 (60%), while the main duct type predominated in Type 2 (64%) (p = 0.039). Conclusions Our novel morphological classification of IPNB correlates with postoperative prognosis and may assist in preoperative planning of surgical strategies for IPNB patients.

Effects of electric field and heat distribution due to trends in metal stent diameter in epicardial pulsed field ablation

Knowledge and attitudes of medical students towards the health impact of climate change: A study from Jordan

Studies have reported a strong relationship between climate change and human health. Medical students’ knowledge and attitudes toward the impact of climate change on health are crucial to fostering their environmental stewardship. Therefore, the aim of this study was to examine the awareness and attitudes of medical students in Jordan toward climate change and human health. The study was cross-sectional in design, anonymous, self-reported, and used a closed-ended questionnaire. The study included 837 students from various medical specialties, including medicine, dentistry, applied medical sciences, pharmacy, and nursing. Statistical analysis involved cross-tabulations and regression analysis. About 46.3% of students reported good awareness of the health impacts of climate change, while 44.8% reported somewhat awareness. This awareness was found to be associated with female gender (P = 0.003) and university level (P < 0.001). In addition, students showed a positive attitude toward the importance of climate change to human health (attitude score = 19.7 out of 24), including the integration of climate change into university curricula. The internet (88.9%) and social media (86.5) were the major sources of information reported by students about climate change. Climate change related illnesses reported by students included air quality/respiratory illnesses, extreme weather-related illnesses, infectious disease outbreaks, physical inactivity, and mental health. In conclusion, medical students in Jordan have an acceptable level of knowledge and positive attitudes toward climate change. This could be improved through interventions that integrate climate change into university curricula.

The role of ChatGPT-4o in differential diagnosis and management of vertigo-related disorders

Observation of Water-Induced Synergistic Acidic Site from NMR-Invisible Al in Zeolite via Solid-State NMR Spectroscopy

Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM

Artificial Intelligence (AI) is being integrated into increasingly more domains of everyday activities. Whereas AI has countless benefits, its convoluted and sometimes vague internal operations can establish difficulties. Nowadays, AI is significantly employed for evaluations in cybersecurity that find it challenging to justify their proceedings; this absence of accountability is alarming. Additionally, over the last ten years, the fractional elevation in malware variants has directed scholars to utilize Machine Learning (ML) and Deep Learning (DL) approaches for detection. Although these methods yield exceptional accuracy, they are also difficult to understand. Thus, the advancement of interpretable and powerful AI models is indispensable to their reliability and trustworthiness. The trust of users in the models used for cybersecurity would be undermined by the ambiguous and indefinable nature of existing AI-based methods, specifically in light of the more complicated and diverse nature of cyberattacks in modern times. The present research addresses the comparative analysis of an ensemble deep neural network (DNNW) with different ensemble techniques like RUSBoost, Random Forest, Subspace, AdaBoost, and BagTree for the best prediction against imagery malware data. It determines the best-performing model, an ensemble DNNW, for which explainability is provided. There has been relatively little study on explainability, especially when dealing with malware imagery data, irrespective of the fact that DL/ML algorithms have revolutionized malware detection. Explainability techniques such as SHAP, LIME, and Grad-CAM approaches are employed to present a complete comprehension of feature significance and local or global predictive behavior of the model over various malware categories. A comprehensive investigation of significant characteristics and their impact on the decision-making process of the model and multiple query point visualizations are some of the contributions. This strategy promotes advanced transparency and trustworthy cybersecurity applications by improving the comprehension of malware detection techniques and integrating explainable AI observations with domain-specific knowledge.

A bacteriolysin of Lactococcus carnosus is potentially involved in mediating contact-dependent antagonism against Listeria monocytogenes

A pre – post quasi-experimental study of team-based learning effectiveness for Vietnamese nursing students

Background Team-Based Learning (TBL) is a student-centered teaching strategy designed to improve problem-solving skills, knowledge, and practical abilities. Despite its increasing use in nursing education globally, limited research has explored its effectiveness in Vietnam. This study evaluates the impact of TBL on learning outcomes, accountability, preferences, satisfaction, engagement, perceptions, and attitudes of Vietnamese nursing students during the Nursing Care for Adults with Internal Medicine course. Methods A quasi-experimental pre-post study was conducted with 186 fourth-year nursing students at a nursing faculty in Vietnam during the 2023–2024 academic year. TBL was implemented in the course, and data were collected using validated instruments, including the Individual Readiness Assurance Test (i-RAT), Team Readiness Assurance Test (t-RAT), Classroom Engagement Survey (CES), and Perceived Collective Efficacy (PCE) scale. Data were collected from January to April 2024 and analyzed using SPSS version 20.0. Results The mean t-RAT scores significantly exceeded i-RAT scores, increasing from 8.17 to 9.68 (t = -19.507, p < 0.001), indicating improved group performance. Students’ attitudes toward teamwork showed significant improvements across all dimensions, with higher post-TBL mean scores. CES and PCE scores also increased significantly post-TBL (31.37 ± 2.002 vs. 29.54 ± 2.186; t = -8.981, p < 0.001; 4.03 ± 0.488 vs. 3.64 ± 0.461, t = -8.667, p < 0.001). Additionally, students reported positive experiences with TBL, with average scores for accountability, preference, and satisfaction at 31.19 ± 2.975, 57.10 ± 5.279, and 36.54 ± 3.815, respectively. Conclusions TBL effectively enhances academic performance, teamwork attitudes, and group responsibility awareness among Vietnamese nursing students. This approach holds promise for improving nursing education in Vietnam, and educators are encouraged to expand its application to other universities and disciplines.

MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules

Investigating the impact of investor attention on AI-based stocks: A comprehensive analysis using quantile regression, GARCH, and ARIMA models

The literature implies an increased interest in AI-based companies, but it is unclear how investor attention affects their volatility. This study fills the gap by investigating the relationship between investor attention, as measured by Google Trends data, and the volatility of AI-based stocks. Using weekly adjusted closing stock price data for 8 AI-based stocks from 2015 to 2024, quantile regression analysis was used to identify the impact of investor attention at various volatility levels. Though the direction of the effect differs, the data shows that investor attention has a considerable impact on the volatility of AI-based companies. Although most stocks show a positive relationship, Tencent Holding’s unique traits or market dynamics impact its response to investor attention. The study uses GARCH and ARIMA models to investigate stock volatility dynamics across time. The findings of this study show that market information changes are critical in driving volatility variations. This study provides insights into the intricate relationship between investor attention and market volatility, with substantial implications for investors and policymakers. Understanding these processes can help investors make educated decisions and allocate resources more effectively, while regulators can devise policies to reduce possible risks and promote market stability.

Large mammal population trends in Comoé National Park (1958–2022): Towards understanding their asymmetric decline and recovery in West Africa’s largest savanna park

Africa’s wildlife decline has received increasing attention, yet underlying reasons have remained opaque. Using generalized additive models of 25 terrestrial and aerial counts, we present West Africa’s first large herbivore population trend series alongside potential drivers. Following Comoé national park’s creation in 1968, large herbivore populations increased till the mid-1980s, but subsequently declined, amplified during Côte d’Ivoire’s political crisis (2002–2011) when active management ceased. Between 2010–2022, populations of roan, hartebeest and waterbuck have quasi-recovered to pre-crisis numbers. The previously dominant kob, common hippopotamus and savanna elephant have remained at c. 10% of their 1970-80s numbers, however. Grasslands declined from 15 to 2% between 1979–2020, negatively impacting kob and common hippopotamus. Since 1962, surrounding human populations and cattle inside the park increased over six-fold, yet the number of rangers only doubled. These developments have resulted in a different wildlife assemblage. Species typical of long-coarse shrub savanna - hartebeest and roan – have reached pre-crisis levels, contrary to kob and common hippopotamus likely because of the reduction of floodplain grasslands and their gregarious distribution rendering them vulnerable to poaching. We recommend increased efforts to understand habitat changes and poaching pressures, prior to re-introducing extinct species. This study highlights the importance but also the challenges of studying large herbivore populations trends alongside drivers of change.

Burden of smoking-related stroke in Saudi Arabia: trends from 1990 to 2021

Background Stroke ranks among the top causes of death and disability globally, with smoking being a significant risk factor for its development. This study aims to assess the impact of smoking-related strokes in Saudi Arabia from 1990 to 2021. Methods The data was extracted from the Global Burden of Disease (GBD) 2021 database. We assessed the burden of smoking-related stroke by estimating the age-standardized rate (ASR) of years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life years (DALYs) and deaths related to this disease. Results From 1990 to 2021, there was a respective 22.37% and 24.15% absolute decrease in the ASR of DALYs and ASR of YLLs, with average annual percentage changes (AAPCs) of -0.85 (-0.88, -0.83) and -0.92 (-0.94, -0.89) attributable to smoking-related stroke in Saudi Arabia. The ASR of death fell absolutely by 28.48%, with AAPC of -1.08 (-1.11, -1.05) between 1990 and 2021. In contrast, the ASR of YLDs absolutely increased by 5.17% during the same period, with an APCC of 0.15 (0.14, 0.17). Further, the decline in AAPCs of ASR of DALYs (-1.99), ASR of YLLs (-1.79), and ASR of deaths (-1.90) was primarily influenced by the reduction in the female population, p < 0.001, except ASR of YLDs, where the increase in the AAPC was attributed to a rise in the male population, p < 0.001. In 2021, YLLs contributed 93% (157.81/179.02) of total DALYs from smoking-related strokes in Saudi Arabia. Death rates rose in all age groups in 2021, with the most significant increases seen in the younger and middle-aged groups (30–59 years). Conclusion While the rates of deaths, DALYs, and YLLs attributable to smoking-related stroke decreased in Saudi Arabia between 1990 and 2021, YLDs significantly increased, mainly in males during the same period. This emphasizes the need for targeted intervention focused mainly on the younger and middle-aged males.

Determinants of subjective total athletic ability

The term “good motor skill” is often discussed in everyday contexts and when observing sports; however, its definition remains elusive, and the associated factors are not well understood. Therefore, in this cross-sectional study, we investigated the determinants of subjective total athletic ability, defined as the sum of subjective athletic abilities across 11 sports disciplines. A sample of 406 undergraduate students completed a questionnaire to evaluate their perceived athletic prowess in various sports, as well as assessments of their personality traits, family background, and sports performance. The analysis revealed correlations between the perceived general athletic ability and specific abilities in soccer, volleyball, basketball, and short-distance racing. Furthermore, linear model analyses indicated a positive association between perceived total athletic ability and personal characteristics such as grit, resilience, and a growth mindset. Factors such as recreational activities in elementary school, sibling structure, prior athletic experience, parental athletic ability, family income, external evaluations of motor skills, and age at first walking also appeared to influence perceived total athletic ability. These results imply that a blend of internal and external factors may shape subjective athletic ability. However, future studies should investigate the causal connections among these factors to deepen our understanding of the concept and its influencers.

The alignment model of indirect communication

Speakers often choose utterances under uncertainty about the potential opinion of the listener. In this case, utterances that do not signal the speaker’s opinion directly may allow the speaker to avoid possible conflict: saying that an election outcome is interesting rather than amazing, even if the speaker is truly excited about it, may give her an option to retreat if it turns out that the listener’s opinion is the opposite. By enhancing the Rational Speech Act framework with a turn-taking pragmatic system, we develop a model of indirect communication that is able to (1) rationalize the choice of indirect utterances when speakers’ opinions do not align; (2) capture complex reasoning about the true interlocutor’s opinion when facing indirect utterances and responses. The model has several novel features: in addition to standard informativeness goals, speaker choices factor in potential divergences of opinions between conversation partners. The listener model further considers multi-turn dialogues rather than isolated utterances: it is able to derive that an utterance like “interesting” can be interpreted positively or negatively depending on preceding discourse. The model, though complex, makes novel, non-trivial qualitative predictions, which are supported by data from three behavioral experiments reported here.

Programmable Solid-State [2 + 2] Photocycloadditions of Dienes Directed by Structural Control and Wavelength Selection

Uncertainty analysis of turbine nozzle guide vane cooling performance

Conjugate heat transfer analysis plays an important role in the design of heavily cooled gas turbine vane or blade. In this study, the aerodynamic and heat transfer performance of the E3 engine nozzle guide vane is investigated through conjugate heat transfer analysis using ANSYS CFX software. Computational fluid dynamic analysis indicates that the shear stress transport turbulence model gives high accuracy in simulating the vane main-stream aerodynamic performance. However, when the simulated surface temperature of the vane was compared with the cooling test data, in which the vane was 3D-printed, significant deviation of the surface temperature distribution was observed. To understand the sources of these deviations, analyses are conducted considering main-stream and coolant flow parameters, as well as manufacturing discrepancies. The results reveal that the large deviation in the manufactured vane (up to 0.5 mm at the leading edge) alters the direction of the coolant flowing out from the leading-edge film-cooling holes, affects the film coverage along the surface, and in consequence, causes the temperature near the stagnation point increasing by approximately 40 K. Furthermore, variations in coolant inlet pressure, decreasing by 10 kPa, and temperature, increasing by 10 K, result in the vane surface temperature increased by 20 ~ 30 K. When the turbulence intensity of the main-stream increased from 5% to 20%, the vane surface temperature increased by approximately 20 K. Hence when conducting conjugate heat transfer analysis to validate the cooling performance of turbine vane or blade, emphasis should be focused on not only the uncertainties of the aerodynamic parameters but the manufacturing deviations.

Correction: The effects of light emitting diodes on mitochondrial function and cellular viability of M-1 cell and mouse CD1 brain cortex neurons

Analysis of the determinants for using health research evidence in health planning in Tanzania: a cross-sectional study

Introduction The use of health research evidence is essential for informed decision-making and effective health planning. Despite its importance, there is limited understanding of the determinants for the use of such evidence in planning processes, particularly in lower-middle-income countries (LMICs) like Tanzania. This study aims to investigate the proportion and determinants that affect the use of health research evidence in health planning in Tanzania. Materials and methods This quantitative study employed a cross-sectional design. Data on health research evidence and the factors influencing its use were collected using a structured questionnaire from 422 healthcare workers involved in planning within 9 regions of Tanzania from October to December 2023. The association between categorical variables was assessed using a chi-square test, while regression analysis was conducted to identify determinants, both at a 95% confidence level, Results The study revealed that 270 (66.2%) of health planning team members strongly agreed that they use health research evidence during planning. Several key determinants were significantly associated with the level of research evidence utilization. These included limited dissemination of research findings (74.5%), inadequate human and non-human resources (70.0%), and insufficient knowledge and training in research (63.7%). A multivariate regression analysis confirmed significant associations between the determinants and the use of research evidence (p<0.05). Descriptive statistics revealed that over 70% of respondents identified the presence of research coordinators, partnerships with universities, availability of research budgets, and internet access as important factors in their research. Inferential analysis indicated that these factors were statistically significantly associated with the use of health research evidence. In addition, more than half of the participants stated motivational factors, such as the presence of continuous quality improvement initiatives, the availability of short- and long-term training programs, on-the-job training opportunities, and incentives like extra duty allowances, as contributors to the enhanced use of research evidence. Bottom of Form Conclusion The study found that planning team members used health research evidence in planning, but several determinants, such as lack of dissemination, resource shortages, and inadequate training, persisted. Interventions should focus on improving dissemination, resources, and training. Future research should explore strategies for enhancing these interventions.

Dissemination of local sub-variants of SARS-CoV-2 detected by detailed mutation analysis in wastewater-based epidemiology

Wastewater-based epidemiology (WBE) is effective for identifying the predominant SARS-CoV-2 variants within specific populations as well as early warning of disease outbreaks. The variant analysis in WBE has been limited to quantifying the proportion of variants, and it has been unable to trace their origins and dissemination pathways. This study aims to elucidate the emergence and transmission of locally predominant SARS-CoV-2 sub-variants through detailed mutation analysis in wastewater genomic surveillance. Genome mutations at each nucleotide position in the S region were examined to identify locally unique sub-variants in geographically distinct cities of Komatsu and Hamamatsu. Notably, the XBT variant, which had never been reported in clinical samples from Japan, was detected in wastewater in Komatsu. Moreover, a unique sub-variant of BA.5 was detected in Komatsu for a duration of 17 weeks whereas it was absent in Hamamatsu. Mutation analysis also revealed significant differences in the duration of the common BA.2.75 sub-variant’s prevalence in Komatsu for 35 weeks, in contrast to only one week in Hamamatsu. These findings underscore the efficacy of wastewater-based genomic epidemiology in identifying the timing of variant entry and prevalence duration, enhancing our understanding of the origins, transmission pathways, and evolutionary trajectories of epidemically important variants.

Environmental resistome–guided development of resistance-tolerant antibiotics

Failure to anticipate new forms of antibiotic resistance has led to resistance developing rapidly to virtually all antibiotics that have entered clinical use. Many of the most problematic types of resistance originated in the environment, where ancient arms races between antibiotic-producing microbes and their competitors have created vast arsenals of antibiotics and resistance. Seizing on the knowledge that resistance in nature is frequently a harbinger of future clinical resistance, we propose introducing an additional step into the antibiotic development process that exploits the susceptibility of development candidates to environmental resistance as a metric for prioritizing lead compounds and as a roadmap for their structural optimization. Using the antibiotic albicidin as a model, we show how the environmental resistome can guide the development of more resistance-tolerant leads. We used metagenomic surveys to identify resistance vulnerabilities for albicidin and guide the synthesis of analogs that evade the resistance threats. We found that natural albicidin analogs (congeners) were especially enriched in structural features that escape resistance, which inspired our syntheses and provided compelling evidence for the evolution of families of antibiotics in response to resistance in nature. The coupling of metagenomics-based resistance surveillance with structural optimizations of new antibiotics is a broadly applicable approach that is easily integrated into antibiotic development programs to generate compounds that are more resilient in the face of resistance.