bluuebunny/crossref_metadata_2025 huggingface dataset:
Morphological, biological, and molecular characterization of Type I granuloviruses of Spodoptera frugiperda
Authors: Ordóñez-García Magali , Bustillos-Rodríguez Juan Carlos , Ornelas-Paz José de Jesús , Acosta-Muñiz Carlos Horacio , Salas-Marina Miguel Ángel , Cambero-Campos Octavio Jhonathan , Estrada-Virgen Mario Orlando , Morales-Ovando Mario Alberto , Velasco Claudio Rios
Published: January 2024

The granuloviruses or GVs (Betabaculovirus) associated with the fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), especially those of Type I, have scarcely been studied but they might represent an alternative for the biocontrol of this insect. In this study, the native granuloviruses SfGV-CH13 and SfGV-CH28 isolated from FAW larvae were characterized for morphology, molecular traits, and insecticidal activity. The elapsed time between symptomatic infection of larvae and stop feeding as well as the weight of larvae before death or prior to pupation were also evaluated. Both granuloviruses isolates showed ovoid shape with a length of 0.4 µm. They showed the same DNA restriction profiles and their genome sizes were about 126 kb. The symptomatic infection with tested GVs mainly caused flaccidity of larva body and discoloration of integument. The integument lysis was only observed in 8% of infected larvae. Infected larvae gradually stopped feeding. Overall, these symptoms are characteristic of infections caused by Type I granuloviruses, which are known as monoorganotropic or slow-killing. The median lethal doses (LD50) values for SfGV-CH13 and SfGV-CH28 isolates were 5.4 × 102 and 1.1 × 103 OBs/larva, respectively. The median lethal time (LT50) ranged from 17 to 24 d. LT50 values decreased as the viral dose was increased. The elapsed time since symptomatic infection until pupation (LD50) and body weight of larvae (third instar) were higher with SfGV-CH28 than SfGV-CH13. Both granulovirus isolates were able to kill the FAW larvae from the 12th day.
CovidSafe: A Deep Learning Framework for Covid Detection Using Multi-Modal Approach
Authors: Panigrahi Srikanth , Behera Chandan kumar , Routhu Srinivasa Rao
Published: January 2024

This work introduces CovidSafe, a framework designed to classify COVID patients as positive or negative based on CXR images. With the global impact of COVID-19, there is a need for effective tools to aid in patient diagnosis. CovidSafe utilizes a multi-model deep feature extractor, combining features from the ResNet family and an autoencoder. The ResNet 18, ResNet 34, and ResNet 50 architectures are employed, and model weights are fine-tuned for optimal performance. The framework leverages the ResNet family models and theautoencoder to extract relevant features. The features from intermediate layers in Resnet family are extracted to capture increasingly complex and high-level information. Similarly, the bottleneck features from Autoencoder are extracted to capture compressed, abstract representation and important features of the image for the classification. These features are then utilized for analysis and classification within the CovidSafe framework. To ensure accessibility for online users, the proposed model is integrated into a web application. Through experimental analysis, the performance of the proposed model is evaluated with various metrics. Results indicate that CovidSafe outperforms baseline models, achieving a precision of 98.67%, recall of 98.62%, and F1-score of 98.64%. These promising results highlight the efficacy of CovidSafe in accurately classifying COVID patients based on CXR images, offering a valuable tool in the fight against the ongoing pandemic.
Substrate promiscuity of key resistance P450s confers clothianidin resistance whilst increasing chlorfenapyr potency in malaria vectors
Authors: Tchouakui Magellan , Ibrahim Sulaiman , Mangoua Mersimine , Thiomela Ricardo , ASSATSE Tatiane , Ngongang-Yipmo Sonia , Muhammad Abdullahi , Mugenzi Leon J. M. , Menze Benjamin , Mzilahowa Themba , Wondji Charles
Published: January 2024

Novel insecticides were recently introduced to counter pyrethroid resistance threats in African malaria vectors. To prolong their effectiveness, potential cross-resistance from promiscuous pyrethroids metabolic resistance mechanisms must be elucidated. Here, we demonstrate that the duplicated P450s, CYP6P9a/b, proficient pyrethroid metabolizers, are reducing neonicotinoid efficacy in Anopheles funestus while exacerbating the potency of Chlorfenapyr. Clothianidin resistance was strongly linked to CYP6P9a/-b genotypes with homozygote resistant individuals significantly surviving more than other genotypes. An additive effect of both genes was observed as double homozygote resistant genotypes were more resistant to clothianidin than other genotype combinations. However, homozygote resistant mosquitoes were more susceptible to chlorfenapyr than homozygote susceptible using laboratory and field tests. Transgenic expression of CYP6P9a/-b in Drosophila confirmed that flies expressing both genes were significantly more resistant to neonicotinoids than controls whereas the contrasting pattern was observed for chlorfenapyr. This phenotype was also confirmed by RNAi knock-down experiments. In vitro expression of recombinant CYP6P9a and metabolism assays established that it significantly depletes both clothianidin and chlorfenapyr, with metabolism of chlorfenapyr producing the insecticidally active intermediate, metabolite tralopyril. This study highlights the risk of cross-resistance between pyrethroid and neonicotinoid and reveals that chlorfenapyr-based control interventions such as Interceptor G2 could remain efficient against some P450-based resistant mosquitoes.
AI Based Model for Prediction of Heavy Metals Using Physio-Chemical Characterization of Agricultural Waste Ashes
Authors: Abbass Wasim , Ahmed Muneeb , Ahmed Ali , Aslam Fahid , Aziz Iram , Mohamed Abdullah
Published: January 2024

The escalating volume of waste materials generated as byproducts is a growing concern in the context of recycling. These waste materials encompass a variety of heavy metals (HMs) that pose significant environmental hazards to plants, animals, and ecosystems. To address that HMs, there was a need to develop an artificial intelligence-based model capable of predicting the presence and quantity of HMs based on the chemical composition of the discards as AWAs. This study delved into a comprehensive analysis of the diverse origins of AWAs, exploring their multifaceted characteristics across different sources. In this research, a total of thirty-two types of SCBA and RHA were accumulated from various sources. The properties and attributes of residual ashes were assessed utilizing various methods of analysis, including X-ray fluorescence (XRF), Fourier-Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscope (SEM), Energy dispersive X-Ray (EDX), X-ray Diffraction Analysis (XRD), Thermogravimetric Analysis / Differential Scanning calorimetry (TGA/DSC), and Atomic Absorption Spectroscopy (AAS). The results were presented in the light of existing literature and standards. The results accordingly revealed that AWAs can be categorized in three fractions based on loss on ignition. At the end some, recommendations for the utilization of SCBA and RHA based on the characterization results were also made for utilization as supplementary material in construction industry. Moreover, the machine learning model was constructed using input variables such as the physio-chemical properties of SCBA and RHA, element properties, and total HMs concentrations to predict the HM fractions. The application of machine learning tool to procured SCBA and RHA revealed that the model utilizing deep neural networks demonstrated performance robustly, possessing strong generalization capabilities (R2 = 0.99 on the testing set), enabling the rapid and accurate prediction of HMs fractions. The element properties were found to be the primary determinant of the HMs fractions. This study adds value to the creation of sustainable approaches for managing waste and provides a framework for the characterization of waste ashes for potential utilize as a primary substance in construction materials.
Brusatol hinders the advancement of bladder cancer by Chac1/Nrf2/SLC7A11 pathway
Authors: Yu Xi , HE ZIQI , WANG QINGHUA , KE SHUAI , WANG HUAXIN , WANG ZHONG , LI SHENGLAN
Published: January 2024

Background
Bladder cancer, also known as BCa, is a common tumor that impacts the urinary system. It is marked by a significant fatality rate and an unfavorable outlook. Promising antineoplastic properties are exhibited by brusatol, which is obtained from the dried ripe fruit of Brucea javanica. The present study aimed to evaluate the influence of brusatol on the advancement of BCa and uncover the molecular mechanism involved.
Materials and methods
We used Cell Counting Kit-8, colony formation and EdU assays to detect cell activity and degree of apoptosis. We used transwell migration assay to detect cell migration and invasion ability. The mechanism of brusatol inhibition of BCa proliferation was studied by flow cytometry, western blotting.
Results
It was revealed that brusatol could reduce the viability and proliferation of BCa cells. A transwell migration assay revealed that brusatol was able to attenuate the invasion and migration of BCa cells. In addition, treatment with RSL3 or ferrostatin-1 enhanced or reversed the brusatol-induced inhibition of BCa cells. While testing for indicators related to iron death it was determined that treatment with brusatol increased the levels of reactive oxygen species, malondialdehyde and Fe2+ in BCa cells. Mechanistically, brusatol induced ferroptosis by upregulating the expression of ChaC glutathione-gpecific gamma-glutamylcyclotransferase (Chac1) in BCa cells. Moreover, treatment with brusatol significantly suppressed the tumor growth in nude mice.
Conclusions
To summarize, the findings of this research demonstrated that brusatol hindered the growth of BCa and triggered ferroptosis by increasing the expression of Chac1. Brusatol has the potential to be considered as a promising candidate for treating BCa.
The Expression and Prognostic Significance of P3H1 in Bladder Cancer: A Bioinformatics Analysis and In Vitro Experiments
Authors: Meng Shuang , Li Zhaowei
Published: January 2024

Background
Bladder cancer (BLCA) is associated with poor prognosis and the identification of new prognostic markers is needed. This study aimed to investigate the significance of Prolyl 3-hydroxylase 1 (P3H1) as a potential prognostic biomarker for BLCA.
Methods
The expression levels of P3H1 in BLCA and normal bladder tissues were analyzed using TCGA database, and P3H1's prognostic value was determined using Kaplan-Meier survival analysis. GSEA analysis was performed to investigate its biological functions. Assessment of the tumor microenvironment was conducted using the CIBERSORT algorithm, and the results were verified through RT-PCR and western blotting. Functional analysis of P3H1 was carried out using Colony-formation, CCK-8, wound healing, transwell, and flow cytometry assays.
Results
The study found that P3H1 was upregulated in BLCA tissues, and elevated expression was linked to an unfavorable prognosis. GSEA analysis revealed putative involvement of P3H1 in the JAK-STAT and WNT signaling pathways. P3H1 expression was also strongly associated with the infiltration of diverse immune cells and immune checkpoints.
Conclusion
Finally, knocking down P3H1 expression effectively suppressed the ability of BLCA cells to proliferate and migrate, while promoting apoptosis. In conclusion, P3H1 has the potential to serve as a prognostic biomarker for BLCA.
Organic Matter Additions for Improved Revegetation of Arsenic-Rich Waste Rock with Planted Boreal Conifers: A Three-Year In Situ Monitoring Study
Authors: Taurines Simon , Guittonny Marie , Séguin Armand
Published: January 2024

Mining waste creates challenging soil conditions that hinder tree establishment for boreal forest restoration. This study investigated the effects of adding topsoil or ramial chipped wood (RCW) on the physicochemical properties of waste rock and the growth and survival of planted native tree species. An randomized-block setup with four treatments and repetitions was established on a gold mine site in western Quebec, Canada in 2018, and planted with Pinus banksiana and Abies balsamea. Results demonstrated that topsoil addition significantly improved height and diameter growth, aerial and root biomasses, survival, and nutrient uptake (N, P, and S) in conifer seedlings, concomitantly to increased water content, decreased pH, and elevated nutrient concentrations in the substrate. However, multivariate analysis revealed that these improved soil conditions alone did not determine the survival and growth of conifer seedlings. In contrast, the application of RCW-based treatments had no discernible impact on the growth and survival of the planted trees. Additionally, topsoil addition effectively reduced the concentration of potentially phytotoxic elements in soil and needles, particularly arsenic. The total arsenic concentration in the mineral substrate (84.1 to 507 µg.g− 1) emerged as a growth-limiting factor for both conifer species. The total concentration of arsenic in the waste rock correlated positively with arsenic accumulation in the tree needles, indicating potential root uptake of this element. This study emphasizes the significance of addressing arsenic availability during reclamation efforts at mine sites. Nonetheless, further research is required to determine the phytotoxic thresholds of arsenic on conifers and its potential metabolic effects.
Evaluation of thalamic volume in patients diagnosed with ESES using the MRI-Cloud method
Authors: Güngör Olcay , Güngör Gülay , Kaban Yunus Emre , Orhan Onur , Aygün Danış , Yüzbaşı Beste Kipçak , Sağtaş Ergin , Egemen Emrah
Published: January 2024

Purpose
Benign epilepsy with centrotemporal spikes (BECTS) is one of the most common epilepsy syndromes in childhood. Accordingly, the main aim of this study was to investigate brain anatomical differences in patients with BECTS using MRI-Cloud, a more objective and quantitative evaluation method for the first time in the literature.
Methods
We included 27 patients diagnosed with BECTS and 27 healthy subjects in the study. We analysed magnetic resonance imaging (MRI) scans of patients diagnosed with BECTS for the first time via the "MRI-Cloud" method.
Results
The mean age of the 54 patients in our study was 8.0 (4–14) years for patients with BECTS, 7.75 (5–14) years for patients with ESES and 8.0 (4–14) years for the control group. Total relative thalamic volume was significantly lower in patients with ESES than in healthy controls (2045.57 ± 343.84 vs. 8775.85 ± 3687.58, p < 0.001), and the total relative thalamic volume was significantly lower in patients with ESES than in patients with BECTS without ESES (2045.57 ± 343.84 vs. 7829.60 ± 3152.85, p < 0.001). The thalamus volumes in patients with ESES were significantly lower than those in patients with BECTS without ESES (right thalamus: 1028 ± 260 vs. 3844 ± 1572, p < 0.001; left thalamus: 1017 ± 150 vs. 3985 ± 2135, p < 0.001).
Conclusion
In this study, we emphasize the contribution of volume analysis and the MRI-Cloud method for detecting microstructural changes at the thalamic level in ESES patients even when conventional MRI findings are normal.
Prediction method for residual stress evolution caused by rolling/sliding contact in ultrasonic vibration-assisted grinding
Authors: Ye Shi Jie , Jun Wen , Tang Jin Yuan , Zhou Wei Hua
Published: January 2024

Ultrasonic vibration-assisted grinding (UVAG) technology has advantages in improving part surface integrity. Currently, the calculation of residual stresses in UVAG typically relies on commercial finite element software. However, because commercial finite element software is not specialized for residual stress calculations, the computational efficiency could be higher, and the accuracy could be better. This paper proposes a semi-analytical residual stress prediction model that considers the Influence of ultrasonic vibration. This paper introduces a semi-analytical residual stress prediction model that considers the impact of ultrasonic vibration. The model calculates the grinding mechanical and thermal stress fields separately using contact mechanics and finite difference methods. Based on Hertz's contact theory and Timoshenko's thermoelastic theory, a correlation model is provided for the association between mechanical and thermal loads with the internal stresses in the workpiece. Subsequently, the residual stress field is solved by considering the thermal-mechanical coupling effects in UVAG. Ultrasonic-assisted grinding experiments were conducted using 12Cr2Ni4A alloy steel as the object, and the maximum error between the predicted residual stresses by the model and the measured residual stresses was 10.5%.In addition, the detailed discussion on the Influence of grinding parameters on residual stress reveals that reducing the grinding wheel speed can significantly increase surface compressive residual stress. The paper's research shows that the residual stress distribution obtained by the new residual stress calculation model is correct, and the calculation efficiency is high, providing a new method for predicting residual stress in UVAG.
Extracellular vesicles of patients on peritoneal dialysis inhibit the TGF-β and PDGF-B mediated fibrotic processes
Authors: Szebeni Beáta , Veres-Székely Apor , Pap Domonkos , Bokrossy Péter , Varga Zoltán , Gaál Aniko , Mihály Judith , Pállinger Éva , Takács István M , Pajtók Csenge , Bernáth Mária , Reusz György S. , Szabó Attila J. , Vannay Ádám
Published: January 2024

Background: Peritoneal fibrosis develops in 50%-80%, and life-threatening encapsulating peritoneal sclerosis (EPS) in 0.5-4.4% of the patients on peritoneal dialysis (PD). Here we investigated the role of extracellular vesicles (EVs) on the transforming growth factor (TGF)-β and platelet derived growth factor (PDGF)-B driven processes of peritoneal fibrosis.
EVs were isolated from the peritoneal dialysis effluent (PDE) of children receiving continuous ambulatory peritoneal dialysis (PD). The impact of PDE-EVs on the epithelial-mesenchymal transition (EMT) and collagen production of the peritoneal mesothelial cells and fibroblasts were investigated in vitro, and in vivo in the chlorhexidine digluconate (CG) induced mice model of peritoneal fibrosis.
Results: PDE-EVs showed spherical morphology in the 100 nm size range, their spectral features, CD63, and annexin positivity was characteristic to EVs. PDE-EVs penetrated into the peritoneal mesothelial cells and fibroblasts and reduced their PDE or PDGF-B induced proliferation. Furthermore, PDE-EVs inhibited the PDE or TGF-β induced EMT and collagen production of the investigated cell types. PDE-EVs contributed to the mesothelial layer integrity and decreased submesothelial thickening of CG-treated mice.
Conclusions: We demonstrated that PDE-EVs significantly inhibit the PDGF-B or TGF-β induced fibrotic processes in vitroand in vivo, suggesting that EVs may contribute to new therapeutic strategies to treat peritoneal fibrosis and other fibroproliferative diseases.
Dynamic Bayesian Network Structure Learning Based on an Improved Bacterial Foraging Optimization Algorithm
Authors: Meng Guanglei , Cong Zelin , Li Tingting , Wang Chenguang , Zhou Mingzhe , Wang Biao
Published: January 2024

With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.
Ground State and Symmetry Breaking of Schr¨odinger-poisson-slatter Equations
Authors: Dong Fangyuan , Wang Zhaoyang
Published: January 2024

We study the nonlocal Schr\"{o}dinger-Poisson-Slater type equation
$$-\Delta u+\omega u+\lambda(I_\alpha\star |u|^q)|u|^{q-2} u=|u|^{p-2}u, $$
where $u \in \dot{H^1}(\mathbb{R}^N) \cap L^p(\mathbb{R}^N), p>1, \omega \in \mathbb{R}, I_\alpha$ is the Riesz transform, and $q \geq 1$. Exploring the equation based on the sign of the real number $\omega$ reveals varied existence outcomes. We establish the existence of global minimizers for $\omega =1$ or $\omega = 0$, depending on the intervals where the generalized Gagliardo-Nirenberg inequalities hold for powers $p$ and $q$. Moreover, symmetry-breaking occurs within large balls, signifying the presence of both radial and non-radial minimizers for the equation in the ball. Additionally, we prove the existence of groundstates and provide a proof of their orbital stability.
Desert ants (Melophorus bagoti) learn from experience to improve waste dumping
Authors: Deeti Sudhakar , McLean Donald James , Cheng Ken
Published: January 2024

The Central Australian red honey-pot ant Melophorus bagoti maintains non-cryptic ground-nesting colonies in the semi-desert habitat. An intriguing aspect of their behaviour is the management of waste, unwanted food, dead nestmates, and some other wastes, typically deposited at distances > 5 m from the nest entrance. In a largely observational study, we found a difference in the waste disposal tactics between naive and experienced ants. Naive individuals, lacking prior exposure to the outdoor environment around the nest, exhibit a less efficient dumping process, characterized by much scanning and meandering during waste disposal. In contrast, experienced ants dump waste with straighter paths and a notable absence of scanning behaviour. Furthermore, experienced dumpers deposit waste at a greater distance from the nest compared to their naive counterparts. These differences suggest that in red honey ants, learning supports waste disposal, with dumping being refined through experience. At the focal nest, curiously, no naive dumper took any learning walks before their first dumping trip, seeming to ‘learn on the job’ of their first trip away from the nest.
Dynamics of traveling wave solutions of conformable time-fractional ISLW and DJKM equations via a new expansion method
Authors: Kırcı Özlem , Koç Dilara Altan , Bulut Hasan
Published: January 2024

This work searches the new wave solutions of time-fractional ion sound and Langmuir waves (ISLWs) equation and time-fractional Date-Jimbo-Kashiwara-Miwa (DJKM) equation with the conformable derivative. The (m+1/G')-expansion method is employed for these models for the first time. Getting support from a symbolic software some new solutions are presented which will be constructive for studies in the field of nonlinear physical phenomena. The regarded method may provide a framework for the exact solutions of several evolution equations in applied science. Moreover, the graphical illustrations are also demonstrated to observe the dynamical behaviours of the solutions.
A first report of Pseudosuccinea columella (Say, 1817), an alien intermediate host for fascioliasis, in Malawi
Authors: Jones S. , Juhász A. , Makaula P. , Cunningham L. J. , Archer J. , Nkolokosa C. , Namacha G. , Kambewa E. , Lally D. , Kapira D. R. , Chammudzi P. , Kayuni S. A. , Musaya J. , Stothard J. Russell
Published: January 2024

Starting in October 2021, quarterly malacological surveys have been undertaken in Malawi, sampling 12 specified freshwater habitats throughout a calendar year. Each survey monitors the presence of aquatic intermediate snail hosts of medical and veterinary importance; in March 2023, the alien lymnaeid species Pseudosuccinea columella was first encountered in Nsanje District. This species identity was later confirmed upon DNA analysis of mitochondrial ribosomal 16S sequences. In July 2023, P. columella was also noted at single sites within Mangochi and Chikwawa Districts, and again in Nsanje District, with an additional location observed. Of particular importance, our sampled location in Mangochi District was directly connected to Lake Malawi which expands the species list of invasive molluscs in this lake. Whilst P. columella is a well-known intermediate snail host for human and animal fascioliasis, screening collected snails for trematode cercariae, alongside molecular xenomonitoring, did not yield equivocal evidence of active fluke infection. However, the newly recognized presence of this alien intermediate snail host within Lake Malawi, and along the Shire River Valley, flags a new concern in altered local transmission potential for human and animal fascioliasis.
Translation, cross-cultural adaptation, and validation of the Duke Activity Status Index (DASI) to Sinhala language: Translation, cross-cultural adaptation, and validation study
Authors: Ranasinghe C. , Kariyawasam K. , Liyanage J. , Walpita Y. , Rajasinghe U. , Abayadeera A. , Chandrasinghe P. , Gunasekara M. , Kumarage S. , Silva M. , Ranathunga K. , Deen K. , Ismail H.
Published: January 2024

Background
Duke Activity Status Index (DASI) is a widely used tool to assess functional capacity among patients, but there is no Sri Lankan version validated for patients in Sri Lanka. This study aimed to cross culturally adapt and test validity and reliability of Sinhala version of DASI (DASI-SL).
Methods
The questionnaire was translated using forward and backward translation methods and cultural adaptation was conducted. It was pretested on ten preoperative patients and further modified. Construct validity and reliability of DASI-SL was assessed by administering the modified final DASI-SL on eighty-one patients who were awaiting non-cardiac surgeries at university surgical wards, National Hospital of Sri Lanka, and Colombo North Teaching Hospital Sri Lanka.
Results
The Cronbach's α coefficient for the internal consistency of DASI-SL was 0.861. The concurrent validity of DASI-SL was substantiated by positively correlating (p < 0.01, rs = 0.466) with the physical subscale of SF-36. There was a significant difference (p < 0.01) in the total score of DASI-SL between two age groups.
Conclusions
The Sri Lankan version of the DASI appears to be a valid, reliable and easy to administer tool to assess functional capacity among patients who are awaiting non-cardiac surgeries.
Why do farmers wait so long before adopting fruit tree-based agroforestry technologies in Malawi? An application of hazard duration analysis
Authors: Mazungwi Batson , Njoloma Joyce , Khataza Robertson , Mwase Weston
Published: January 2024

Fruit tree-based agroforestry has been widely promoted but its adoption is generally slow and low. We used a hazard duration analysis to determine factors that influence time until adoption of fruit tree-based agroforestry in Malawi. A Weibull-Gamma model mixture was fitted to account for the unobserved heterogeneity. The results show that households take 4 years, on average, to start adopting fruit tree-based agroforestry. Further, the results show that farmer’s education, club membership, participation in training programmes and provision of material incentives (tree germplasm) are likely to shorten waiting time until the adoption of fruit tree-based agroforestry. The findings imply that there is a need to promote programmes that organise farmers into purpose-driven groups, offer tailor-made agroforestry training and, where possible, provide incentives for quick adoption of fruit tree-based agroforestry.
The Double Burden of Normal Weight Obesity (NWO) and Normal Weight Central Obesity (NWCO) on Hypertension Risk: A Cross-sectional Study
Authors: M Yogesh , Makwana Naresh , Patel Nirmalkumar Shaileshbhai
Published: January 2024

Background
Normal weight obesity (NWO) and normal weight central obesity (NWCO) have emerged as risk factors for cardiovascular disease. However, the association between the prevalence of these two conditions and hypertension risk has not been well studied, especially in Asian populations.
Methods
In this community-based cross-sectional study, we assessed body composition and blood pressure in 1000 adults aged > 20–65 years. NWO was defined as a normal body mass index (BMI 18.5–22.9 kg/m2) plus a high body fat percentage (> 20.6% in men, > 33.4% in women). NWCO was defined on the basis of a normal BMI plus high waist circumference (≥ 90 cm in men, ≥ 80 cm in women). Hypertension and prehypertension were defined as per the JNC 7 guidelines.
Results
The prevalence of NWO was 38%, affecting 228 of 600 normal-weight individuals. The prevalence of normal weight central obesity was 32% (192 of 600 participants with a normal BMI NWO and NWCO had drastically elevated hypertension odds of 3.2 (95% CI 2.1–4.7) and 3.5 (95% CI 2.3–5.2) times versus metabolically healthy normal weight people, respectively, independent of confounders). The odds for prehypertension were also greater for NWO (OR 1.7, 95% CI 1.1–2.6) and NWCO (OR 2.0, 95% CI 1.3–3.2).
Conclusion
A high proportion of normal-weight adults in this population have elevated cardiovascular risk based on excess body fat and abdominal adiposity. Assessing body composition would better identify at-risk individuals missed by BMI categorization alone. Reducing the double burden of NWO and NWCO may help lower the prevalence of hypertension in the population.
Genistein demonstrates superb binding ability against Mpro: A Monte Carlo approach for developing novel SARS-CoV-2 inhibitors
Authors: Olugbogi Ezekiel Abiola , Kikiowo Babatomiwa , Lawler Destiny Nicola , Arietarhire Leviticus , Enya Joseph
Published: January 2024

Background SARS-CoV-2, commonly known as the novel coronavirus or COVID-19, represents a highly contagious virus responsible for a global pandemic. Its suspected origin in bats led to its first identification in Wuhan, China, in late 2019. Primarily transmitted through respiratory droplets from infected individuals coughing or sneezing, the virus manifests symptoms such as fever, cough, and shortness of breath. Severe cases may escalate to pneumonia and respiratory failure. With a global death toll surpassing 6,937,771, the urgent quest for effective treatments continues. To address this critical need, our study systematically explores the computational screening of 52 flavonoids as potential SARS-CoV-2 inhibitors, emphasizing minimal adverse effects compared to conventional anti-viral drugs, which often present associated complications.
Results Following the preparation of 58 ligands and the protein, a computational screening was conducted on flavonoids and standard drugs against the crystal structure of the SARS-CoV-2 Main Protease (7KYU) utilizing the virtual screening workflow (VSW) within Maestro 12.8 Schrodinger suite. Notably, only XC4, the co-ligand, and genistein successfully passed the VSW assessment. The selected ligands underwent additional screening processes, including PASS prediction, DFT, and ADMET profiling. Genistein exhibited superior results in terms of biological activity, binding energy, and binding free energy. The bioactivity scores further underscored the suitability of genistein for various biological functions.
Conclusion The result of this study provide support for the refinement of genistein as a potential inhibitor for SARS-CoV-2. Consequently, we hold a favorable view of genistein.
TransCDR: a deep learning model for enhancing the generalizability of drug activity prediction through transfer learning and multimodal data fusion
Authors: Xia Xiaoqiong , Zhu Chaoyu , Zhong Fan , Liu Lei
Published: January 2024

Accurate and robust drug response prediction is of utmost importance in precision medicine. Although many models have been developed to utilize the representations of drugs and cancer cell lines for predicting cancer drug responses (CDR), their performances can be improved by addressing issues such as insufficient data modality, suboptimal fusion algorithms, and poor generalizability for novel drugs or cell lines. We introduce TransCDR, which uses transfer learning to learn drug representations and fuses multi-modality features of drugs and cell lines by a self-attention mechanism, to predict the IC50 values or sensitive states of drugs on cell lines. We are the first to systematically evaluate the generalization of the CDR prediction model to novel (i.e., never-before-seen) compound scaffolds and cell line clusters. TransCDR shows better generalizability than 8 state-of-the-art models. TransCDR outperforms its 5 variants that train drug encoders (i.e., RNN and AttentiveFP) from scratch under various scenarios. The most critical contributors among multiple drug notations and omics profiles are Extended Connectivity Fingerprint and genetic mutation. Additionally, the attention-based fusion module further enhances the predictive performance of TransCDR. TransCDR, trained on the GDSC dataset, demonstrates strong predictive performance on the external testing set CCLE. It is also utilized to predict missing CDRs on GDSC. Moreover, we investigate the biological mechanisms underlying drug response by classifying 7,675 patients from TCGA into drug-sensitive or drug-resistant groups, followed by a Gene Set Enrichment Analysis. TransCDR emerges as a potent tool with significant potential in drug response prediction. The source code and data can be accessed at https://github.com/XiaoqiongXia/TransCDR.
How proximal ulnar shaft fracture could easily result in radial head dislocation? A critical value analysis for proximal ulnar fracture in Monteggia fracture
Authors: Huang Kuan-Yu , Chen Chun-Yu , Lin Kai-Cheng
Published: January 2024

Background
Monteggia fracture is defined as a combination of proximal ulnar fracture with radial head dislocation. Radial head dislocation may be misdiagnosed under elbow X-ray, leading to further complications. This study aimed to analyze the fracture pattern between extra-articular Monteggia fracture and an isolated proximal ulnar fracture to determine the difference in terms of how close the fracture site is to the coronoid and what type of fracture pattern is more likely to result in radial head dislocation.
Methods
This is a single-center, retrospective study. In 2014/01-2022/06, adult patients with fractures in the proximal to midshaft ulna were collected. Nonacute trauma, revision surgeries, and intra-articular ulnar fractures were excluded. All patients received elbow X-rays in AP and lateral views, and forearm X-rays. The fracture pattern and injury mechanism were recorded.
Results
We included 51 patients in this study. The mean patient age was 38.5 ± 16.1 years. 70.1% were men (n = 36). Extra-articular Monteggia fractures were classified according to the Bado classification into type I (n = 5), type II (n = 7), type III (n = 8), and type IV (n = 0). 63% were traffic accidents (n = 32). 45% were oblique type ulnar fractures(n = 23). Monteggia fracture and proximal ulnar fracture presented with mean ulnar fragment lengths of 6.12 ± 2.32 cm and 9.00 ± 3.00 cm (p < 0.01). As per the ROC, a proximal ulnar fragment length of 7.325 cm had the highest value for the AUC (0.807). Angulations of Monteggia fracture and ulnar fracture were 24.02 ± 12.10° and 10.77 ± 8.10° (p < 0.01). However, there were no differences in the length of the fracture line between two groups.
Conclusions
Fractures of the proximal ulna within 7.325 cm from the coronoid are more likely to cause an extra-articular Monteggia fracture than a pure proximal ulnar fracture. Otherwise, a Montelggia fracture is prone to more severe angulation.
Effect of simulation-based learning on student self-efficacy in cardiopulmonary physiotherapy education
Authors: Choi Ching Man
Published: January 2024

Background
Simulation-based learning is a common mode of learning in many healthcare programmes. There is a growing popularity of utilising simulation in physiotherapy education as it provides students with more and safer practising opportunities. Whilst there are promising results regarding simulation-based learning in physiotherapy, it is uncommon in local physiotherapy programmes especially cardiopulmonary physiotherapy course in Hong Kong and its effect on students is unknown.
Objective
The aim of this study was to investigate the effect of simulation-based learning in student self-efficacy in cardiopulmonary physiotherapy.
Method
Third year physiotherapy students at Caritas Institute of Higher Education in Hong Kong attended a series of simulation-based practice sessions as one of the components of their cardiopulmonary physiotherapy course. They were then given survey to complete to rate about the experience and their self-efficacy after attending the session.
Result
Students found the simulation session including pre-briefing and debriefing was beneficial to their learning, self-efficacy and skill competency.
Conclusion
Students think simulation is beneficial to their learning and skills development. More research is needed to ensure the effectiveness of simulation-based learning in enhancing student self-efficacy.
Temporary Drainage Clamping After Total Hip Arthroplasty: A Meta- Analysis of Randomized Controlled Trials
Authors: Lin Youbin , Zhu Xiaoying , He Shijie , Shen Jiquan , Zhou Changjian , Wang Bo
Published: January 2024

Background
This meta-analysis was performed to assess the outcomes of clamping the drainage tube in patients after THA.
Methods
We systematically searched the following databases: Cochrane Central Register of Controlled Trials, PubMed, Web of Science, Embase, and Ovid. Included patients were prepared for THA and separated into two groups: the intervention group (the drainage tube was clamped) and the control group (the drainage tube was not clamped). Studies that compared drainage volume, total blood loss, allogeneic blood transfusion, and adverse events were included in this meta-analysis. Meta-analysis was performed by RevMan 5.3 software.
Results
Four randomized controlled trials (RCTs) involving 223 patients (the intervention group = 110; the control group = 113) were finally included in this meta-analysis. Meta-analysis indicated that clamping the drainage tube after THA significantly decreased the drainage volume and total blood loss compare to the control group (P < 0.05). However, there was no statistically significant difference in the percentage of patients receiving allogeneic blood transfusion between the two groups (P = 0.10). Clamping the drainage tube did not increase the rate of adverse complications compare to the control group.
Conclusions
Clamping the drainage tube decreases the drainage volume and total blood loss with no increase in adverse complications after THA.
Beyond Limits: Charting New Horizons in Glioma Tumor Classification through Hybrid Quantum Computing with The Cancer Genome Atlas (TCGA) Data
Authors: Akpinar Emine , Oduncuoglu Murat
Published: January 2024

Gliomas, which are the most common malignant primary brain tumors, present significant challenges in terms of varying survival rates, treatment modalities, and prognostic processes between patients with low-grade gliomas (LGGs) and high-grade gliomas (HGGs). Accurate classification and grading of LGGs and HGGs are crucial for appropriate treatment planning and assessment of overall prognosis. The classification and grading of gliomas have undergone evolution over time, and the inclusion of molecular markers in the classification of glioma tumors by the WHO Central Nervous System (CNS) Tumors Classification in 2016 and the incorporation of advanced molecular diagnostics in 2021 have improved glioma tumor characterization. However, the high cost and limited accessibility of molecular genetic tests, coupled with the time-consuming nature of obtaining results, can lead to delays in critical treatment decisions. To address these challenges, various classical artificial intelligence methods, such as machine learning and deep learning, have been applied to problems in this field, yielding a certain level of success. Nevertheless, the continuous expansion of medical data dimensions, the inherent noise level in the data, and the limitations of the classical vector space pose significant challenges that classical artificial intelligence methods struggle to overcome. Recent studies have demonstrated that the use of quantum computing and quantum artificial intelligence technologies in healthcare not only addresses these problems but also accelerates complex data analyses and processes large datasets more efficiently. This paper presents a novel hybrid quantum (or classical-quantum) computing model aimed at differentiating between LGGs and HGGs using data from The Cancer Genome Atlas (TCGA). To the best of our knowledge, this study is the first to investigate the classification of LGGs and HGGs using a hybrid classical-quantum framework within the TCGA dataset. In the classical part of the study, an ensemble feature selection method was used to identify the most important molecular markers and clinical features within the TCGA glioma dataset. In the quantum section, six variational quantum classifier (VQC) models with different hyperparameters are proposed. These classifiers are subsequently used to differentiate between LGGs and HGGs using the features obtained from the ensemble model. The computational results show that among the six VQC models, the VQC-1 model, which incorporates Rx and CX gates in the feature map and Ry, Rz and CY gates in the parameterized quantum circuit and utilizes the AQCD optimization method, achieves the highest classification accuracy of 0.74. This study provides a novel perspective on the classification of glioma tumors by combining classical and quantum computing methods.
The Evolution of Tumor Enucleation Partial Nephrectomy: A Comparison of Perioperative Outcomes for Sutureless Hemostatic Bandage as an Alternative to Standard Renorrhaphy
Authors: Rac Goran , Ellis Jeffrey L. , Lanzotti Nicholas J. , McCormick Mallory E. , Chen Victor , Felice Michael D. , Hartman Natalie , Janakiraman Sarang , Desai Shalin , Halgrimson Whitney , Patel Hiten D. , Gupta Gopal N.
Published: January 2024

Background
The standard approach to hemostasis during partial nephrectomy (PN) is to perform suture renorrhaphy (SR). Application of a hemostatic bandage (HB) is an alternative maneuver to minimize blood loss and devitalized renal parenchyma. We aim to evaluate perioperative outcomes of PN with tumor enucleation (TE) comparing SR to HB.
Methods
We analyzed a retrospective cohort of 195 patients undergoing robot-assisted laparoscopic PN with TE performed by a single surgeon at a tertiary referral center (2012–2022). Hemostasis of the enucleation bed was obtained with SR in 54 patients while 141 patients underwent application of HB consisting of Surgicel®, Gelfoam® soaked in thrombin, and Floseal®. Patient factors, tumor characteristics, and perioperative outcomes were compared using Student’s t-tests and chi-squared tests. Temporal trends were evaluated using Spearman coefficients.
Results
Over time, there was a significant decrease in utilization of SR in favor of HB (p < 0.001). SR patients had tumors of greater complexity by RENAL nephrometry score compared to HB patients (p < 0.001). Operative time (141 vs 183 min, p < 0.001), warm ischemia time (11.6 vs 24.2 min, p < 0.001), estimated blood loss (37 vs 214 mL, p < 0.001), and length of stay (1.2 vs 1.8 days, p < 0.001) favored the HB group. There was no significant difference in major Clavien-Dindo grade ≥ 3 complications (p = 0.22). Renal function was comparable with mean estimated glomerular filtration rate decrease of 0.66 and 0.54 mL/min/1.73m2 at 3-months postoperatively for HB and SR, respectively (p = 0.93).
Conclusions
Application of a HB is a safe alternative to SR for hemostasis following PN with TE in appropriately selected patients.
Analyzing Neural Network Algorithms for Improved Performance: A
Computational Study
Authors: Ramaj Vehbi , Elezaj Rame , Čajić Elvir
Published: January 2024

Machine learning is an area of artificial intelligence that deals with the development of algorithms and models for automatically detecting patterns and making inferences from data. Neural networks are one of the most popular machine learning models that simulate the learning process of the brain and are widely used in various fields such as pattern recognition, prediction and control. Matlab is a popular programming language in the field of machine learning due to its ease of use and numerous libraries that contain the implementation of various machine learning algorithms.
In this paper, we will present the simulation of machine learning in neural networks using different algorithms in Matlab. We will describe several algorithms such as feedforward neural network, convolutional neural network and deep neural network. Also, we will show how these algorithms are applied in practice using different datasets. Finally, we will compare the performance of different algorithms and analyze their advantages and disadvantages.