bluuebunny/crossref_metadata_2025 huggingface dataset:
Polysorbate-80 coated Galantamine-Chitosan nanoparticles for enhanced safety and efficacy: Pharmacokinetics and Brain distribution in Rats
Authors: Natarajan Tamilselvan , Chellan Vijaya Raghavan , Rahman Habibur , Siram Karthick , Karuppaiah Arjunan , Gopinathapillai Prasanth Kalavalarasariel
Published: November 2024

In brain drug delivery, the BBB (Blood-Brain Barrier) has been a great barrier for various active pharmaceutical drugs. Hence, developing suitable drug delivery systems are need of the hour. However, the present study aimed to develop a polysorbate-80 (PS-80) coated galantamine (GAL) loaded chitosan nanoparticles (CS-NPs) with particles less than 200 nm to enhance the drug internalization in the brain. GAL-CS-NPs were prepared using the ionic gelation technique and optimized to obtain a particle size of less than 200nm and further coated wirh PS-80 to obtain polysorbate-80 coated galantamine loaded chitosan nanoparticles (PS-80-GAL-CS-NPs) The physiochemical properties of uncoated GAL-CS-NPs, PS-80 coated GAL-CS-NPs and the in-vitro evaluations, such as cytotoxicity and cellular internalization in SH-SY-5Y human neuroblastoma cell lines, were studied. The particle size of optimized PS-80-GAL-CS-NPs was observed to be 115±4 nm, and zeta potential was found to be 31.2±1.7 mV. While the drug entrapment efficiency was found to be 65.5±1.2 %. The in-vitro drug release of PS-80-GAL-CS-NPs was found to be 56.75±1.3 %. However, the cytotoxicity studies did not show any toxicity for the drug concentrations of 10 and 100 µg/mL. PS-80-GAL-CS-NPs facilitated time-dependent GAL uptake in SH-SY-5Y cell lines. Studies conducted in vivo in plasma revealed a steady release of GAL from PS-80-GAL-CS-NPs. The distribution PS-80- GAL-CS-NPs in the brain after oral administration at 1st, 4th and 8th hr was found to be 1.7, 3.1 and 2.0 folds higher when compared to the uncoated NPs. Further, the histopathological study did not show any morphological changes in the rat brain. This study indicates that PS-80-GAL-CS-NPs might be a promising delivery method for Alzheimer’s Disease (AD)
Exploring the Correlation Between Gallstone Disease and Sarcopenia Risk: Insights from a Cross-Sectional Analysis
Authors: Zeng Di , Lu Jiong , Chen Jinhong
Published: November 2024

Background Musculoskeletal disorders related to chronic liver and biliary diseases are increasingly recognized, with a growing body of observational studies linking these conditions. This study investigates the association between gallstone disease and sarcopenia, exploring the implications for patient health outcomes.Methods A cross-sectional analysis was conducted involving 1,683 participants to assess the relationship between gallstones and sarcopenia. Adjusted odds ratios (OR) for sarcopenia were calculated using logistic regression models, controlling for sociodemographic and health-related factors.Results Among the participants, those with gallstones had a higher likelihood of sarcopenia, with adjusted odds ratios of 2.15 in Model 1 and 2.71 in Model 2. The risk was particularly pronounced in females, with an odds ratio of 2.756. Additionally, individuals with diabetes faced an elevated odds ratio of 8.576 for developing sarcopenia. Although trends in increased risk were observed in other subgroups, such as those with hypertension and younger adults, these did not achieve statistical significance. An inverse relationship was also noted between appendicular skeletal muscle mass (ASM) and gallstone occurrence.Conclusion This study demonstrates that gallstone disease increases the risk of sarcopenia, highlighting the need for integrated management strategies that include nutritional evaluations and exercise interventions. Further longitudinal research is essential to clarify causal relationships and underlying mechanisms.
Longitudinal qualitative assessment of meaningful symptoms and relevance of WATCH-PD digital measures for people with early Parkinson’s
Authors: Mammen Jennifer R. , Lerner Aaron , Al-Rubayie Raunak , Kostrzebski Melissa , Stephenson Diane , Xiao Yuge , Redmond Chris , Tyo Mirinda , Reddy Varun G. , Auinger Peggy , Dorsey E. Ray , Adams Jamie L.
Published: November 2024

Background: Longitudinal qualitative data on what matters to people with Parkinson's disease are lacking and needed to guide patient-centered clinical care and development of outcome measures.
Objective: To evaluate change over time in symptoms, impacts, and relevance of digital measures to monitor disease progression in early Parkinson’s.
Methods: In-depth, online symptom mapping interviews were conducted with 33 people with early Parkinson's at baseline and one year later to evaluate (A) symptoms, (B) impacts, and (C) relevance of digital measures to monitor personally relevant symptoms. Maps and transcripts were coded for frequencies, Likert scale rankings (0=Not present to 4=Most bothersome), and thematic findings. Wilcoxon Signed Rank test was used to evaluate change over time.
Results: Other than walking and balance, most motor symptoms did not change significantly from baseline to one year later. Multiple significant changes were observed in non-motor areas (cognition, speech, sleep, mood, fatigue, pain; p<0.05) and functional impacts (mobility, effort to do usual activities, personal comfort; p<0.05). Thematic analysis revealed ability to cope with and compensate for actual or anticipatedsymptoms reduced disruptions to well-being and changed how bothersome symptoms were. All digital measures targeted symptoms that were personally important to most participants (>80%).
Conclusion: Non-motor and walking/balance symptoms changed sooner than other motor symptoms during the course of one year. Evaluation of coping and compensatory mechanisms may be essential to understanding symptom bothersomeness at a given point in time. Smartphone and smartwatch digital measures were relevant to personally meaningful symptoms of early PD.
Topological linear response of hyperbolic Chern insulators
Authors: Sun Canon , Chen Anffany , Bzdusek Tomas , Maciejko Joseph
Published: November 2024

We establish a connection between the electromagnetic Hall response and band topological invariants in hyperbolic Chern insulators by deriving a hyperbolic analog of the Thouless-Kohmoto-Nightingale-den Nijs (TKNN) formula. By generalizing the Kubo formula to hyperbolic lattices, we show that the Hall conductivity is quantized to -e^2C_{ij}/h−e2Cij/h, where C_{ij}Cij is the first Chern number. Through a flux-threading argument, we provide an interpretation of the Chern number as a topological invariant in hyperbolic band theory. We demonstrate that, although it receives contributions from both Abelian and non-Abelian Bloch states, the Chern number can be calculated solely from Abelian states, resulting in a tremendous simplification of the topological band theory. Finally, we verify our results numerically by computing various Chern numbers in the hyperbolic Haldane model.
Robust independent validation of experiment and theory: Rivet version 4 release note
Authors: Bierlich Christian , Buckley Andy , Butterworth Jonathan Mark , Gütschow Christian , Lönnblad Leif , Procter Tomasz , Richardson Peter , Yeh Yoran
Published: November 2024

The Rivet toolkit is the primary mechanism for phenomenological preservation of collider-physics measurements, containing both a computational core and API for analysis implementation, and a large collection of more than a thousand preserved analyses. In this note we summarise the main changes in the new Rivet 4 major release series. These include a major generalisation and more semantically coherent model for histograms and related data objects, a thorough clean-up of inelegant and legacy observable-computation tools, and new systems for extended analysis-data, incorporation of preserved machine-learning models, and serialization for high-performance computing applications. Where these changes introduce backward-incompatible interface changes, existing analyses have been updated and indications are given on how to update new analysis routines and workflows.
Codebase release 4.0 for RIVET
Authors: Bierlich Christian , Buckley Andy , Butterworth Jonathan Mark , Gütschow Christian , Lönnblad Leif , Procter Tomasz , Richardson Peter , Yeh Yoran
Published: November 2024

The Rivet toolkit is the primary mechanism for phenomenological preservation of collider-physics measurements, containing both a computational core and API for analysis implementation, and a large collection of more than a thousand preserved analyses. In this note we summarise the main changes in the new Rivet 4 major release series. These include a major generalisation and more semantically coherent model for histograms and related data objects, a thorough clean-up of inelegant and legacy observable-computation tools, and new systems for extended analysis-data, incorporation of preserved machine-learning models, and serialization for high-performance computing applications. Where these changes introduce backward-incompatible interface changes, existing analyses have been updated and indications are given on how to update new analysis routines and workflows.
THE ROLE OF EMOTIONAL INTELLIGENCE IN CRISIS LEADERSHIP: EFFECTS ON EMPLOYEE MORALE AND ORGANIZATIONAL RESILIENCE
Authors: Sharma Akshit
Published: November 2024

This research paper examines the role of emotional intelligence (EI) in crisis leadership and its influence on employee morale and organizational resilience. By analyzing theoretical frameworks such as Golemans Emotional Intelligence Model, Transformational Leadership Theory, and Crisis Management Theories, the study establishes the significance of EI as a critical asset for leaders navigating crises. The paper delves into how emotionally intelligent leaders-who demonstrate empathy, self-awareness, and effective communication-enhance employee morale, create a supportive work environment, and build trust during times of uncertainty. Key findings indicate that leaders with high EI can mitigate the negative emotional impacts of crises, thereby preserving morale, fostering a sense of security, and promoting collective resilience. Furthermore, the research reveals that emotionally intelligent leadership not only addresses the immediate emotional needs of employees but also contributes to long-term organizational resilience by fostering a culture of psychological safety and adaptability. Through real-world case studies, the paper contrasts the outcomes of EI-driven leadership with those of low EI leadership, highlighting the far-reaching impact on employee engagement, productivity, and overall organizational stability. The implications suggest that developing emotional intelligence in leaders is essential for maintaining a motivated and resilient workforce capable of navigating complex and unpredictable business environments. Ultimately, the research underscores the strategic importance of integrating emotional intelligence into crisis leadership to ensure sustainable organizational success and employee well-being.
IDENTIFICATION OF THE AETIOLOGY OF GASTROINTESTINAL PERFORATION: CLINICAL AND MDCT CLUES
Authors:   Post Graduate, Department of General Surgery, Basaveshwara Teaching and General Hospital, Attached Mahadevappa Rampure Medical College, Kalaburagi., B. Nethravathi , Y. Patil Suresh ,   Professor&Head, Department of General Surgery, Basaveshwara Teaching and General Hospital, Attached to Mahadevappa Rampure Medical College, Kalaburagi., Umar Farooque Mohd ,   Post Graduate, Department of General Surgery, Basaveshwara Teaching and General Hospital, Attached Mahadevappa Rampure Medical College, Kalaburagi.
Published: November 2024

A common medical emergency that has a high fatality rate between 30 and 50 % is perforation of the gastrointestinal tract (GIT). Oesophageal perforations can cause rapid chest pain, odynophagia, and vomiting gastroduodenal perforations can cause acute, severe abdominal pain and colonic perforations typically advance more slowly, resulting in localized abscesses or secondary bacterial peritonitis. Sepsis, an abscess that resembles an abdominal mass, or delayed symptoms are among the subsets of people who may present. Ancillary findings may indicate underlying problems that require more examination after initial closure of the ruptured bowel, while direct multidetector computed tomography (MDCT) findings confirm the diagnosis and localize the perforation site. Findings from MDCT scans include extraluminal gas, gut wall thickening, apparent discontinuity in the intestinal wall, extraluminal contrast, aberrant mural enhancement, localized fat stranding and/or free fluid, and localized abscess or phlegmon in confined perforations. In order to highlight the MDCT and clinical signs suggestive of the underlying aetiology and localization of the perforated site, this paper will examine the range of MDCT results found in GIT perforation. I. KEY POINTS Extraluminal gas, wall discontinuity or thickness, and fat stranding are signs of a GIT perforation. There is a spectrum of abundance to absence in pneumoperitoneum and extraluminal oral contrast. Additional observations include masses, faecal impaction, ischemia, foreign substances, and excessive wall thickening. The presence of a ruptured peptic ulcer suggests supramesocolic pneumoperitoneum and hyperenhancing gastric wall. Iatrogenic perforation is indicated by ascites and/or persistent or growing free air after surgery.
DUFOUR, SORET AND DISSIPATION EFFECTS ON MAGNETOHYDRODYNAMIC FLOW PAST A STRETCHING PERMEABLE SHEET WITH RADIATIVE CHEMICAL REACTION ANDHEAT SOURCE
Authors:   Lecturer in Mathematics, Government Polytechnic, Daman - 396210, India., M. Kayalvizhi , N. Indumathi ,   Assistant Professor, Department of Mathematics, Sri Ramakrishna Engineering College, CBE- 641 022, India., P. Renuka ,   Assistant Professor, Department of Applied Mathematics, KPR Institute of Engineering and Technology, Coimbatore - 641 407, India., R.J. Bhargavi ,   Lecturer, Department of Chemical Engineering, Government Polytechnic, Daman - 396210, India., Veni V. Krishna ,   Lecturer, Department of Civil Engineering, Government Polytechnic, Daman - 396210, India.
Published: November 2024

Present investigation explores the numerical solution of Magnetohydrodynamic laminar steady gray fluid flow with heat and mass transfer past a stretching permeable sheet, incorporating several factors such as heat source, viscous and joules dissipation effects, thermal radiation, chemical reactions, Soret and Dufour effects. The governing nonlinear PDEs are simplified into ODEs with the help of similarity transformations. Momentum equation is solved analytically. Non-Dimensional temperature and concentration calculated with the help of RK- shooting method with Nachtsheim-Swigert iteration method. Key results include the calculation of heat and mass transfer rates and Co-efficient of skin friction. The effects of various parameters such as magnetic field strength, suction, radiation, chemical reactions, internal heat generation and dissipative effects on the flow characteristics and other physical parameters are thoroughly investigated, highlighting their impact on the thermal and concentration profiles in the system. This comprehensive analysis provides insights into the behaviour of the fluid flow under the specified conditions, which can be useful for applications in engineering and industrial processes.
COMPLEMENTARY LEARNING RESOURCES IN GRADE 8 MATHEMATICS
Authors:   Mathematics Department, Sorsogon National High School, Sorsogon City, Philippines 4700., Ebio Arvin , A. Deri Ritzelda ,   Graduate School, Sorsogon State University, Sorsogon City, Philippines 4700.
Published: November 2024

In a spiral progression curriculum, all learning competencies at every grade level must be completely covered within a school year. Hence, all competencies at every grade level must be completely covered within a school year. This study aims to develop complementary learning resources (CLeaR) in Grade 8 Mathematics, a learning material intended for the uncovered learning competencies. The 4D development model was utilized in the study. The respondents were teachers and experts. The data were obtained using unstructured interviews and DepEd LRMDS standards for Print Resources which was modified to include other factors. The study found that the CLeaR: (a) comprises 12 self-directed worksheets intended for the 12 uncovered learning competencies; (b) has two main features designed to serve its purpose; and (c) passed all the standards for print resources and other factors. It can be concluded that the main purpose of developing the CLeaR is for the learners to acquire all the competencies in Grade 8 Mathematics. Teachers are encouraged to use the CLeaR to achieve this goal. Doing so would at the same time test the effectiveness of the developed learning resource and other necessary features may be introduced or added.
PREDICTING STUDENT PERFORMANCE IN ONLINE LEARNING PLATFORMS: ANALYZING ENGAGEMENT METRICS WITH MACHINE LEARNING MODELS
Authors: Aggarwal Riyan
Published: November 2024

The emergence of online learning platforms harbours both opportunities and challenges, this paper focuses on machine learning algorithms for deep diving into key predictors of students success in online environments, captured by engagement metrics such as quiz scores, participation in forums, and time spent on learning tasks. Data was collected through a survey of 50 students and then run through the Random Forest model. Major predictors of success include the assignment approach, quiz score, and motivation. The prediction from this model can be interesting because it can pick important features, but the overall accuracy is quite low at about 20%, which implies that larger datasets and more features might be necessary to enhance the predictions. Time management, motivation, and staying in touch are such variables that the research holds key to determining results for students. The results are important not only for online learning platforms but also for interventions suggested, which include early warning systems, personalised learning paths, and elements of gamification to support performance for improved student support at the right time for those most at risk. Future studies should consider more sophisticated machine learning models and the use of more objective performance data in their attempt to make more refined predictions.
Residential income tax compliance in Nepal: An empirical analysis
Authors: Dev Lamichhane Basu , Bahadur Lama Padam , Pathak Bishnu , Kumar Chataut Mukund
Published: November 2024

Tax compliance is the determining factor in tax report submission, enhancing tax awareness. A low level of tax compliance (TC) directly affects the government revenue and low inflow of information regarding taxpayers. This study uses descriptive and explanatory research approaches to identify determinants of residential rental income tax compliances (TC) among property owners in Kathmandu Metropolitan City, Nepal. A total of 1,129 structured questionnaires were distributed among the households of Kathmandu Valley for the cross-sectional and primary data, and only 500 (44.29 percent) useful responses were received from the respondents as the population was unknown. The response rate exceeds the estimated sample size of 384. The findings revealed a significant positive impact of tax rate on tax compliances (r = 0.329**, P<0.05). Fines and penalty (FP) and tax compliance (TC) found a positive and significant association depicting (r =.398**, P<0.05). Similarly, there was a positive and significant relationship (r = .612**, P<0.05) between attitudes and perception (AP) and tax compliance (TC). Moreover, a positive and significant association was found (r =.410**, P<0.05) between tax knowledge (TK) and tax compliance (TC). Furthermore, tax policy (TP) was positively and significantly associated (r = .440**, P<0.05) with tax compliance (TC). This study enhances taxpayers’ knowledge and helps to execute the policies to increase the efficiency and transparency of the tax system in Nepal.
Acknowledgment The authors would like to express their gratitude to Prof. Dr. Achyut Gnawali and Mr. Prem Bahadur Budhathoki, Associate Professor, and Dr. Gangaram Bishowakarma, Assistant Professor of Tribhuvan University, Kathmandu, Nepal, for extending warm support towards the research work through the academic inputs.
Employing the agricultural water footprint concept to enhance the sustainable management of water resources: a review
Authors: Mizyed Amjad , Moghier Yunes , Hamada Mazen
Published: November 2024

Agriculture is recognized as a significant consumer of water, with projections indicating an increase in consumption due to rising food demand and the impacts of climate change. Therefore, every country must ensure the adequacy and security of water for sustainable uses. This research reviews the water footprint (WF) index as a recent water indicator that measures the human appropriation of water resources for several consumptive uses that qualify decision-makers to manage water efficiently in agriculture sectors. Light is shed on the WF concept, virtual water, mechanisms of water use, and the applications in water resources management, as well as methods for assessment with its three components, blue, green, and gray. The comparison between these methods was discussed, and the differences and motives for use were clarified. The good innovation in this review is linking sustainability considerations with the WF, discussing measuring the sustainability of agricultural activities, and deducing the best policies and decisions to meet environmental and economic needs without compromising the future requirements. This outcome confirms the effectiveness of applying the WF in enhancing sustainability.
Mass transfer process in the adsorption of Cibacron Blue (CB) on untreated pea pods (UPPs): kinetic and thermodynamic studies
Authors: Abbas Moussa , Trari Mohamed
Published: November 2024

In recent decades, there has been extensive use of synthetic dyes in the dye-based industries, particularly the textile sector. Therefore, the aim of this study was to evaluate the adsorption of Cibacron Blue (CB) onto untreated pea pods (UPPs) in a batch system. The effects of the initial CB concentration (10–20 mg/L), solution pH (2–12), adsorbent dose (0–4 g/L), particle size (50–500 μm), and temperature (295–318 K) on the CB adsorption were investigated in batch configuration to determine the optimum conditions. Analyses of UPPs were characterized by Fourier Transform Infrared (FTIR) spectroscopy, Brunauer–Emmett–Teller (BET), and point-of-zero charge (pHpzc = 5.6). Under optimized conditions (pH: 2.5, particle size: 50 μm, time: 40 min, adsorbent dose: 2.5 g/L, and agitation speed: 250 rpm), up to 30.30 mg/g at 25 °C is removed from the solution. The adsorptions kinetics obey rather a pseudo-second-order kinetic model with a determination coefficient of R2 = 0.999. The adsorption isotherms have been used for the determination of thermodynamic parameters, i.e. the negative free energy ΔG° (−4.33 to 0.783 kJ/mol), negative enthalpy change ΔH° (−54.63 kJ/mol), and entropy (ΔS° = −0.1705 kJ/mol.K) indicate that the CB adsorption onto UPPs is spontaneous and exothermic in nature.
A universal empirical equation to estimate the abundance of carbapenem-resistant genes during aerobic digestion of wastewater sludge
Authors: Poorasgari Eskandar , Örmeci Banu
Published: November 2024

Carbapenem-resistant genes (CRGs) exist in wastewater and accumulate in wastewater sludge. Due to the potential threat posed by the CRGs, it is important to quantify CRGs and predict their removal and discharge concentrations during aerobic sludge digestion. Nonetheless, gene quantification is tedious, error-prone and expensive. This study aims to develop multiple regression models to estimate CRGs from sludge parameters that are routinely measured for the monitoring and design of aerobic sludge digesters. Batch reactors were operated at mesophilic and thermophilic temperatures for 20-35 days. Sludge samples were periodically taken during aerobic digestion. Three CRGs (blaGES, blaOXA-48 and blaIMP-27) together with 16S rRNA and integron class 1 genes were quantified. Aerobic digestion reduced the abundance of all target genes. Multiple regression modelling was conducted in linear (LM) and non-linear (NLM) modes. Sums of squared errors of the LM models were 0-0.048, whereas those of the NLM models were 0–0.003. Adjusted R2 ranges of the LM and NLM models were 0.774–0.931 and 0.986–1, respectively. Overall, the NLM models predicted the abundance of target genes more accurately than the LM models. NLM models may be used to modify the design and operational parameters of aerobic sludge digesters.
AS DANÇAS-CANCÕES NYANEKA NA PREPARAÇÃO CORPÓREO-VOCAL DO ATOR ANGOLANO
Authors: MÁQUINA NELSON SAMPAIO , Haderchpek Robson Carlos
Published: November 2024

O presente artigo é resultado da pesquisa de mestrado Dança-canção: As canções ritualísticas nyaneka nas recriações do Bimphadi defendida no Programa de Pós-Graduação em Artes Cênica da UFRN. Partindo de uma metodologia empírica de caráter experimental, propusemos a desconstrução das formas do teatro europeu, impostas aos atores angolanos pelo colonizador, e buscamos um caminho próprio, pautado nas nossas experiências e na nossa cultura. A partir da pesquisa prática desenvolvida pelo Grupo Bimphadi, constatamos que as danças-canções, decorrentes das manifestações culturais nyanekas, trazem em si elementos espetaculares que podem ser ressignicados na preparação corpóreo-vocal do ator angolano.
Network Pharmacological Analysis of Hydroxychloroquine Intervention in the Treatment of Iga Nephropathy
Authors: Zou Mengxiao , Xu Gang , Ge Shuwang , Guo Kanglin , Duo Qian , Cheng Yichun
Published: November 2024

Background:

IgA nephropathy (IgAN) is the most prevalent primary glomerulonephritis globally
and has a high propensity to develop into end-stage renal disease (ESRD). Hydroxychloroquine has been
proven to reduce proteinuria in IgAN patients, but the precise mechanism remains unclear. Therefore, network
pharmacology was used to investigate the mechanism.

Methods:

PubChem and SwissADME databases were utilized to acquire the structure of hydroxychloroquine.
The SwissTargetPrediction, PharmMapper, DrugBank, TargetNet, and BATMAN-TCM databases were then
utilized to obtain the targets. The target genes related to IgAN were then gathered from the databases, which
included GeneCards, PHARMGKB, DrugBank, OMIM, and DisGeNET. Common targets were obtained by
UniProt. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses
were performed to define the main molecular mechanisms and pathways. Furthermore, a protein-protein interaction
(PPI) network was constructed using the STRING tool, and the core targets were obtained by Cytoscape.
Finally, molecular docking between the core targets and hydroxychloroquine was performed.

Results:

167 common target genes were acquired by overlapping. The core targets were TNF, ALB, IL1B,
JUN, FOS, SRC, and MMP9. The GO and KEGG results showed the targets to be related to the production of
inflammatory cytokines and chemokines and were engaged in the toll-like receptor (TLR) signaling pathway.
At the same time, the molecular docking results showed that the core targets all combined with hydroxychloroquine
closely.

Conclusion:

This study proved that hydroxychloroquine may treat IgAN through the TLR signaling pathway,
and the restraint of TNF, TLR, IL1B, and JUN may be essential for the treatment.
Effects of Gadolinium Chelate Administration Timing on T2-weighted and Diffusion-weighted Abdominal MRI Examination: A Prospective Study
Authors: Jia Su-Lan , Xu Hui , Yang Da-Wei , Ren A-Hong , Yang Zheng-Han
Published: November 2024

Background:

Magnetic Resonance Imaging (MRI) data acquisition includes several sequences that might be optimized to reduce the scan time.

Objective:

This study aimed to investigate the impact of gadolinium chelate administration timing on scan duration and image quality in Diffusion-weighted
Imaging (DWI) and T2-weighted Imaging (T2WI) during abdominal MRI examinations.

Methods:

A prospective study was conducted from October 2018 to May 2020. Study participants were assigned into a conventional group, undergoing MRI
with DWI and T2WI sequences pre and post-gadolinium injection, or an optimized group, receiving MRI with DWI and T2WI sequences after
gadolinium injection. Quantitative image quality, measured by the Signal-to-noise Ratio (SNR), Contrast-to-noise Ratio (CNR), and Apparent
Diffusion Coefficient (ADC), was analyzed. Kappa statistics were employed for the inter-observer agreement on liver lesion detection.

Results:

Our study has included 341 patients, with 168 and 173 in the conventional and optimized groups, respectively. Mean scan durations were 1,304
(±143) and 1,015 (±129) s for the conventional and optimized groups, respectively (p<0.05). For the liver, spleen, and pancreas, SNR and ADC
remained statistically unchanged in post-enhanced DWI and T2WI (p>0.05). Significant decreases in the SNR and ADC of the kidney were
observed in post-contrast DWI and T2WI (p<0.05). Hepatic lesion detectability did not show significant differences between pre and post-contrast
DWI and T2WI images (p>0.05).

Conclusion:

DWI and T2WI sequences assessed post-gadolinium administration exhibited shortened scan time without compromising the image quality for
liver, spleen, and pancreas evaluations. However, these sequences should be examined before gadolinium administration when assessing the
kidneys.
Bioactive Compounds Aimed at the AMPK Pathway: A Window into their Therapeutic Potential
Authors: Chakraborty Debarupa Dutta , Chakraborty Prithviraj , Sarma Tanmay , Hazarika Nilutpal , Bania Tina , Rynjah Mevareen
Published: November 2024

Current investigations have proposed that focusing on malignancy cell metabolism is
an elective restorative methodology in cancer treatment. AMPK is the significant energy sensor
regulating ordinary as well as malignancy cell metabolism. Drugs as AMPK activators can quell
malignancy cell development through the various signalling cascade. AMPK enactment in light
of natural AMPK activators, for example, BME, ICT, Thymoquinone, has been displayed to
constrict mTOR, approving that BME can restrain cell development in ovarian malignant
growth cells utilizing suppressing mTOR-mediated protein translation process. Nutraceuticals as
well as traditional medicines operate as natural AMPK activators that regulate AMPK movement
through a mechanism that is independent of AMP. These naturally occuring AMPK activators
might straightforwardly actuate AMPK through single or more than one mechanism in
AMPK initiation. Exploring the synergistic effects of compounds with existing cancer treatments
could open new avenues for combinatorial therapy, potentially enhancing efficacy and
reducing side effects. Moreover, understanding the precise molecular pathways through which
these natural AMPK activators exert their effects will be crucial in designing targeted therapies
that maximize their therapeutic potential. The integration of these nutraceuticals into standard
cancer treatment protocols requires rigorous clinical trials to establish their safety, optimal dosing,
and long-term benefits in cancer patients. With further investigation, the possibility of using
natural AMPK activators in addition to traditional medicines could transform cancer treatment
by providing more patient-friendly and comprehensive options.
Research Trends in the Evaluation and Treatment of Speech Disorders Using Automatic Speech Recognition (ASR)
Authors: Kim Ji Youn
Published: November 2024

Purpose: The purpose of this study is to examine recent research trends regarding automatic speech recognition (ASR), which is used in the evaluation and intervention of speech disorders.Methods: Through a search engine, articles published in domestic journals were searched. A total of 27 papers were selected from the searched documents and analyzed according to the year, research subject, speech task, and ASR system.Results: The years with the most research was done in 2019~2021. The subjects who most frequently underwent speech evaluation and treatment using ASR system were those with dysarthria. The speech production tasks used to utilize ASR were at the word and sentence level, and commercialized and non-commercialized ASR systems were used similarly.Conclusion: These results might be used for the fundamental data to establish the most suitable evaluation methods and intervention plans for patients with speech disorders in clinical and research fields.
Enhancing Mandarin Tone and Vowel Recognition in Cochlear Implant Simulation through Combining Task Practice
Authors: Kim Seeon , Gao Qi , Chen Fei , Luo Xin
Published: November 2024

Purpose: Mandarin tone recognition with cochlear implant simulation can be enhanced via targeted auditory training. However, such training generally does not transfer to different tasks, even when they share the same stimuli. As such, this study investigated whether combined Mandarin tone and vowel recognition training leads to greater improvements in Mandarin tone, vowel, and sentence recognition compared to tone- or vowel- only training.Methods: Twenty-nine native Mandarin-speaking young adults with normal hearing were randomly assigned to one of three training regimens: Tone-Only, Vowel-Only, Tone-Vowel. Mandarin tone, vowel, and sentence recognition were tested before and after training.Results: Tone-Only training improved Mandarin tone recognition but not vowel recognition, while Vowel-Only training improved Mandarin vowel recognition but not tone recognition. The combined Tone-Vowel training improved both tone and vowel recognition. ToneVowel training also led to improvements in Mandarin sentence recognition, although these gains were not significantly greater than those observed in Tone-Only or Vowel-Only groups.Conclusion: These findings suggest that within an equivalent amount of training, combined tone and vowel recognition training enhances both tone and vowel recognition, while tone- or vowel-only training improves the performance in the targeted task alone. Combined tone and vowel recognition may be necessary to achieve improvements in Mandarin tone, vowel, and sentence recognition.
A Study on the Rehabilitation Effects of Tinnitus Counseling for Hearing Aid Users Who Suffer from Tinnitus
Authors: You Dong Uk , Bahng Junghwa
Published: November 2024

Purpose: This study aimed to evaluate the efficacy of tinnitus counseling in improving tinnitus symptoms among hearing-impaired individuals who use hearing aids and have reported difficulties due to tinnitus.Methods: Thirty participants completed the tinnitus handicap inventory (THI), the tinnitus magnitude index (TMI), and a tinnitus knowledge questionnaire both before and after tinnitus rehabilitation counseling. A tinnitogram was also assessed. The tinnitus knowledge questionnaire was customized based on a preliminary survey that identified participants’ misconceptions or specific areas of interest regarding tinnitus, enabling targeted and informative counseling.Results: THI scores showed a statistically significant decrease, from a mean of 48.16 (standard deviation [SD], 27.1) to 41.66 (SD, 25.6), with an average reduction of 6.5 points (p = 0.000). Similarly, TMI scores showed a statistically significant reduction, from a mean of 20.7 (SD, 6.1) to 19.0 (SD, 6.58), with an average decrease of 1.7 points (p = 0.01).Conclusion: The findings support the efficacy of tinnitus rehabilitation counseling in alleviating tinnitus-related discomfort among individuals who have used hearing aids for over a year. While the reduction in THI scores was statistically significant, an analysis of the minimum clinically significant difference showed that only 33% of participants exhibited clinically meaningful improvement. Future studies should explore the benefits of continuous counseling for those who do not respond to a single session.
Modulation Interference from Speech-Modulated Noise in Bimodal Cochlear Implant Users
Authors: Han Jae Ho , Choi Hyo Jung , Lee Jae Hee
Published: November 2024

Purpose: This study examined modulation interference in sentence recognition for bimodal cochlear implant (CI) users.Methods: Thirteen bimodal users and thirteen normal-hearing (NH) listeners (reference) participated in this study. The adaptive Korean Matrix sentence-in-noise test measured the speech reception thresholds (SRTs) required for 50% speech-in-noise intelligibility. As background noise, we presented two background noises: stationary speech-shaped noise (SSN) and single-talker speech-modulated masker (International Collegium of Rehabilitative Audiology noise, ICRA). The amount of masking release due to fluctuations in the speech-modulated noise, called speech masking release (SMR), was calculated from the difference between SRTs with SSN and SRTs with ICRA noise. The SRTs were measured twice for bimodal CI users: CI-only and bimodal listening.Results: The mean SMR for NH listeners was approximately 12 dB. The mean SMR for bimodal CI users was about -5 dB, with either CI alone or bimodal listening. This means that bimodal CI users experienced modulation interference rather than masking release from the temporal fluctuations. We observed positive bimodal benefits, yet the interference from fluctuating maskers was similar between the CI-only and bimodal listening. The low-frequency aided hearing thresholds in the non-implanted ear were unrelated to the amount of SMR for bimodal users.Conclusion: NH listeners could take advantage of temporal fluctuations in the speech-modulated noise. However, bimodal CI listeners hardly glimpsed target speech in the dips of the speech-modulated masker, regardless of listening mode. Bimodal listening did not significantly reduce modulation interference compared to CI-only listening.
The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education
Authors: Alli Sauliha Rabia , Hossain Soaad Qahhār , Das Sunit , Upshur Ross
Published: November 2024

In the field of medicine, uncertainty is inherent. Physicians are asked to make decisions on a daily basis without complete certainty, whether it is in understanding the patient’s problem, performing the physical examination, interpreting the findings of diagnostic tests, or proposing a management plan. The reasons for this uncertainty are widespread, including the lack of knowledge about the patient, individual physician limitations, and the limited predictive power of objective diagnostic tools. This uncertainty poses significant problems in providing competent patient care. Research efforts and teaching are attempts to reduce uncertainty that have now become inherent to medicine. Despite this, uncertainty is rampant. Artificial intelligence (AI) tools, which are being rapidly developed and integrated into practice, may change the way we navigate uncertainty. In their strongest forms, AI tools may have the ability to improve data collection on diseases, patient beliefs, values, and preferences, thereby allowing more time for physician-patient communication. By using methods not previously considered, these tools hold the potential to reduce the uncertainty in medicine, such as those arising due to the lack of clinical information and provider skill and bias. Despite this possibility, there has been considerable resistance to the implementation of AI tools in medical practice. In this viewpoint article, we discuss the impact of AI on medical uncertainty and discuss practical approaches to teaching the use of AI tools in medical schools and residency training programs, including AI ethics, real-world skills, and technological aptitude.
An Intelligent System for Classifying Patient Complaints Using Machine Learning and Natural Language Processing: Development and Validation Study
Authors: Li Xiadong , Shu Qiang , Kong Canhong , Wang Jinhu , Li Gang , Fang Xin , Lou Xiaomin , Yu Gang
Published: November 2024

Background

Accurate classification of patient complaints is crucial for enhancing patient satisfaction management in health care settings. Traditional manual methods for categorizing complaints often lack efficiency and precision. Thus, there is a growing demand for advanced and automated approaches to streamline the classification process.

Objective

This study aimed to develop and validate an intelligent system for automatically classifying patient complaints using machine learning (ML) and natural language processing (NLP) techniques.

Methods

An ML-based NLP technology was proposed to extract frequently occurring dissatisfactory words related to departments, staff, and key treatment procedures. A dataset containing 1465 complaint records from 2019 to 2023 was used for training and validation, with an additional 376 complaints from Hangzhou Cancer Hospital serving as an external test set. Complaints were categorized into 4 types—communication problems, diagnosis and treatment issues, management problems, and sense of responsibility concerns. The imbalanced data were balanced using the Synthetic Minority Oversampling Technique (SMOTE) algorithm to ensure equal representation across all categories. A total of 3 ML algorithms (Multifactor Logistic Regression, Multinomial Naive Bayes, and Support Vector Machines [SVM]) were used for model training and validation. The best-performing model was tested using a 5-fold cross-validation on external data.

Results

The original dataset consisted of 719, 376, 260, and 86 records for communication problems, diagnosis and treatment issues, management problems, and sense of responsibility concerns, respectively. The Multifactor Logistic Regression and SVM models achieved weighted average accuracies of 0.89 and 0.93 in the training set, and 0.83 and 0.87 in the internal test set, respectively. Ngram-level term frequency–inverse document frequency did not significantly improve classification performance, with only a marginal 1% increase in precision, recall, and F1-score when implementing Ngram-level term frequency–inverse document frequency (n=2) from 0.91 to 0.92. The SVM algorithm performed best in prediction, achieving an average accuracy of 0.91 on the external test set with a 95% CI of 0.87-0.97.

Conclusions

The NLP-driven SVM algorithm demonstrates effective classification performance in automatically categorizing patient complaint texts. It showed superior performance in both internal and external test sets for communication and management problems. However, caution is advised when using it for classifying sense of responsibility complaints. This approach holds promises for implementation in medical institutions with high complaint volumes and limited resources for addressing patient feedback.
Identification of a Susceptible and High-Risk Population for Postoperative Systemic Inflammatory Response Syndrome in Older Adults: Machine Learning–Based Predictive Model
Authors: Mai Haiyan , Lu Yaxin , Fu Yu , Luo Tongsen , Li Xiaoyue , Zhang Yihan , Liu Zifeng , Zhang Yuenong , Zhou Shaoli , Chen Chaojin
Published: November 2024

Background

Systemic inflammatory response syndrome (SIRS) is a serious postoperative complication among older adult surgical patients that frequently develops into sepsis or even death. Notably, the incidences of SIRS and sepsis steadily increase with age. It is important to identify the risk of postoperative SIRS for older adult patients at a sufficiently early stage, which would allow preemptive individualized enhanced therapy to be conducted to improve the prognosis of older adult patients. In recent years, machine learning (ML) models have been deployed by researchers for many tasks, including disease prediction and risk stratification, exhibiting good application potential.

Objective

We aimed to develop and validate an individualized predictive model to identify susceptible and high-risk populations for SIRS in older adult patients to instruct appropriate early interventions.

Methods

Data for surgical patients aged ≥65 years from September 2015 to September 2020 in 3 independent medical centers were retrieved and analyzed. The eligible patient cohort in the Third Affiliated Hospital of Sun Yat-sen University was randomly separated into an 80% training set (2882 patients) and a 20% internal validation set (720 patients). We developed 4 ML models to predict postoperative SIRS. The area under the receiver operating curve (AUC), F1 score, Brier score, and calibration curve were used to evaluate the model performance. The model with the best performance was further validated in the other 2 independent data sets involving 844 and 307 cases, respectively.

Results

The incidences of SIRS in the 3 medical centers were 24.3% (876/3602), 29.6% (250/844), and 6.5% (20/307), respectively. We identified 15 variables that were significantly associated with postoperative SIRS and used in 4 ML models to predict postoperative SIRS. A balanced cutoff between sensitivity and specificity was chosen to ensure as high a true positive as possible. The random forest classifier (RF) model showed the best overall performance to predict postoperative SIRS, with an AUC of 0.751 (95% CI 0.709-0.793), sensitivity of 0.682, specificity of 0.681, and F1 score of 0.508 in the internal validation set and higher AUCs in the external validation-1 set (0.759, 95% CI 0.723-0.795) and external validation-2 set (0.804, 95% CI 0.746-0.863).

Conclusions

We developed and validated a generalizable RF model to predict postoperative SIRS in older adult patients, enabling clinicians to screen susceptible and high-risk patients and implement early individualized interventions. An online risk calculator to make the RF model accessible to anesthesiologists and peers around the world was developed.