bluuebunny/crossref_metadata_2025 huggingface dataset:
Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention
Authors: He Shi-Hui , Yun Lijun , Yi Hai-Cheng
Published: June 2024

Background

Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction between two drugs, but cannot directly determine their accurate risk level.

Methods

In this study, we propose a multi-class drug combination risk prediction model named AERGCN-DDI, utilizing a relational graph convolutional network with a multi-head attention mechanism. Drug-drug interaction events with varying risk levels are modeled as a heterogeneous information graph. Attribute features of drug nodes and links are learned based on compound chemical structure information. Finally, the AERGCN-DDI model is proposed to predict drug combination risk level based on heterogenous graph neural network and multi-head attention modules.

Results

To evaluate the effectiveness of the proposed method, five-fold cross-validation and ablation study were conducted. Furthermore, we compared its predictive performance with baseline models and other state-of-the-art methods on two benchmark datasets. Empirical studies demonstrated the superior performances of AERGCN-DDI.

Conclusions

AERGCN-DDI emerges as a valuable tool for predicting the risk levels of drug combinations, thereby aiding in clinical medication decision-making, mitigating severe drug side effects, and enhancing patient clinical prognosis.
Exploring the policy implications of household wealth differentials and under-five child deaths in Ghana
Authors: Osei Christian Kwaku , Nketiah-Amponsah Edward
Published: June 2024

Background

Ghana was one of the few countries commended to have halved extreme poverty after the MDG era yet the under-five mortality rate (U5MR) was dire. Ghana’s U5MR is still above the national and global SDG target. As a result, the government has over the years implemented other complementary social and policy interventions to improve the socioeconomic status of households to ensure better child health outcomes. Yet key gaps exist and threaten child health outcome sustainability, and child health-related SDG targets. In this regard, we hypothesize that household wealth should therefore not influence under-five child deaths significantly.

Methods

The paper first reports the under-five mortality trend over the past 30 years in Ghana. It uniquely analyses the levels and trends in wealth as measured by the Gross National Income per capita at purchasing power parity (ln GNI/p, ppp) as a proxy of the mean Comparative Wealth Index (CWI) against under-five child deaths in Ghana. Using data from the 2014 Ghana Demographic and Health Survey and employing the logistic regression estimation technique, the paper further estimates the effect of household wealth and other key covariates on under-five child deaths. Implemented government poverty-alleviating policies and intervention programs to reduce childhood mortality in Ghana were reviewed to identify risk gaps to child deaths.

Results

The 30 year trend analysis from 1990 to 2020 shows that the under-five mortality rate in Ghana is still high, falling short of the rate of decline that is expected toward achieving the 2030 SDG target for U5MR of 25 deaths per 1000 live births. Our empirical estimations show that household wealth still has a negative and significant relationship with under-five child deaths despite the government’s existing poverty-alleviating and pro-poor health policies. In addition, the results show that regional differences, maternal age, the number of children alive, and attendance to postnatal healthcare services are crucial for child survival.

Conclusion

The results imply that the government should consolidate universal programmes with intensified targeted interventions toward improving household wealth, especially in the Northern Savannah regions. A new and dynamic child health policy is imminent to reflect strategies towards achieving the SDG target on U5MR. Additionally, intensive education on active maternal participation in postnatal care should be given critical consideration to improve child survival.
Stereotactic body radiation therapy is beneficial for a subgroup of patients with urothelial cancer and solitary metastatic disease: a single institution real-world experience
Authors: Svedman Fernanda Costa , Holmsten Karin , Jawdat Faith , Hailom Wehazit , Alm Daniel , Grozman Vitali , Ullén Anders
Published: June 2024

Background

Standard treatment options for patients with metastatic urothelial cancer (mUC) include systemic platinum-based chemotherapy, immunotherapy, antibody-drug-conjugates, and targeted therapy. Oligometastatic disease (OMD) may be an intermediate state between localized and generalized cancer. The best treatment strategy for OMD and oligoprogressive (OPD) disease is poorly studied in mUC but local stereotactic body radiation therapy (SBRT) could be an option to avoid or delay systemic treatment. The aim of this study was to assess the efficacy and feasibility of SBRT given in a real-world patient population.

Methods

All patients with mUC treated with SBRT at Karolinska University Hospital, Stockholm, Sweden between 2009 and 2022 were included in this study. Baseline clinical characteristics, treatment data, SBRT dosimetry data and treatment outcome were collected retrospectively. The study endpoints were local control rate (LCR), progression-free-survival (PFS), overall survival (OS) and feasibility of SBRT.

Results

In total 39 patients were treated with SBRT. The median follow-up was 25.6 months. The LCR was 82%. PFS and OS were 4.1 and 26.2 months, respectively. Treatment was well tolerated; all patients but one (treatment related pain) completed the planned SBRT. Number of metastases irradiated with SBRT was significantly associated with outcome; patients with only one irradiated lesion had more favourable PFS compared to individuals with 2 or more metastases (HR 4.12, 95% CI: 1.81–9.38, p = 0.001). A subgroup of patients (15%) achieved a sustained long-term survival benefit and never required systemic treatments after SBRT.

Conclusions

SBRT was well tolerated and associated with high LCR. A subpopulation of patients with single metastatic lesion achieved long-term OS and never required subsequent systemic treatment after SBRT. Prospective randomized studies are warranted to discover treatment predictive biomarkers and to investigate the role of SBRT in oligometastatic UC.
Incidence and treatment-related risk factors of inhibitor development after intensive FVIII replacement for major orthopaedic surgery in previous treated haemophilia A
Authors: Wang Han , Zhu Wei , Wang Shujie , Feng Bin , Weng Xisheng
Published: June 2024

Introduction

Haemophilia A (HA) is an X-linked recessive bleeding disorder caused by lack or deficiency of coagulation factor VIII.

Aim

The aim of this study is to determine the incidence and treatment-related risk factors of inhibitor development after intensive FVIII replacement for major orthopaedic surgery in previous treated persons with HA.

Methods

A total of 151 HA who underwent 221 major orthopaedic surgical procedures after intensive FVIII treatment were reviewed. The results of inhibitor tests were collected. Potential clinical risk factors for inhibitor development were analyzed.

Results

111 people were diagnosed with severe HA. Thirty-seven persons (24.5%) had history of previous intensive FVIII treatment for surgical procedure. They received a mean perioperative cumulative FVIII of 498 iu/kg within first week after surgery. Seven cases (4.6%) developed an inhibitor post-operatively in our study. Surgical procedure for pseudotumor and the group of persons who experienced postoperative complications had the higher incidence of inhibitor development (9.5%, 13.3% respectively). Only previous history for intensive FVIII exposure was considered as a significant predictor for postoperative inhibitor development after multivariate logistic regression analysis (OR: 29.5, P = 0.002).

Conclusion

The incidence of inhibitor development in previously treated persons with HA undergoing major orthopaedic surgery was 4.6% and the history of previous intensive FVIII treatment for surgery was associated with higher risk of inhibitor development.
Alterations in DNA methylation machinery in a rat model of osteoarthritis of the hip
Authors: Kawarai Yuya , Nakamura Junichi , Hagiwara Shigeo , Suzuki-Narita Miyako , Inage Kazuhide , Ohtori Seiji
Published: June 2024

Background

This study aimed to validate alterations in the gene expression of DNA methylation-related enzymes and global methylation in the peripheral blood mononuclear cell (PBMC) and synovial tissues of animal hip osteoarthritis (OA) models.

Methods

Animals were assigned to the control (no treatment), sham (25 µL of sterile saline), and OA (25 µL of sterile saline and 2 mg of monoiodoacetate) groups. Microcomputed tomography scan, histopathological assessment and pain threshold measurement were performed after induction. The mRNA expression of the DNA methylation machinery genes and global DNA methylation in the PBMC and hip synovial tissue were evaluated.

Results

The OA group presented with hip joint OA histopathologically and radiologically and decreased pain threshold. The mRNA expression of DNA methyltransferase (Dnmt 3a), ten–eleven translocation (Tet) 1 and Tet 3 in the synovial tissue of the OA group was significantly upregulated. Global DNA methylation in the synovial tissue of the OA group was significantly higher than that of the control and sham groups.

Conclusions

The intra-articular administration of monoiodoacetate induced hip joint OA and decreased pain threshold. The DNA methylation machinery in the synovial tissues of hip OA was altered.
The deubiquitinating protein OTUD6B promotes lung adenocarcinoma progression by stabilizing RIPK1
Authors: Yang Miaomiao , Wei Yujie , He Xin , Xia Changwei
Published: June 2024

Background

There is growing evidence indicating that deubiquitinating enzymes may contribute to tumor progression and can serve as promising therapeutic targets.

Methods

The overexpression of deubiquitinase OTUD6B in lung adenocarcinoma (LUAD) and its adjacent tissues was analyzed by immunohistochemistry and TCGA/GO database. Survival analysis further supported OTUD6B as a potential target for LUAD treatment. We assessed the effect of OTUD6B on LUAD cell growth using cell viability assays and conducted TUNEL staining, migration, and invasion experiments to investigate the impact of OTUD6B on the apoptosis and metastasis of LUAD cells. Additionally, we established a transplanted tumor model in nude mice to validate our findings in vivo. Finally, using IP mass spectrometry and co-IP experiments, we screened and confirmed the influence of RIPK1 as a substrate of OTUD6B in LUAD.

Results

OTUD6B is highly overexpressed in human LUAD and predicts poor prognosis in LUAD patients. OTUD6B knockdown inhibited the proliferation of LUAD cells and enhanced apoptosis and inhibited metastasis in LUAD cells suppressed. A549 xenografts revealed that OTUD6B deletion can slow down tumour growth. Additionally, OTUD6B can bind to RIPK1, reduce its ubiquitination level and increase its protein stability.

Conclusions

Our results suggest that OTUD6B is a promising clinical target for LUAD treatment and that targeting OTUD6B may constitute an effective anti-LUAD strategy.
Dirus complex species identification PCR (DiCSIP) improves the identification of Anopheles dirus complex from the Greater Mekong Subregion
Authors: Saeung Manop , Pengon Jutharat , Pethrak Chatpong , Thaiudomsup Saranya , Lhaosudto Suthat , Saeung Atiporn , Manguin Sylvie , Chareonviriyaphap Theeraphap , Jupatanakul Natapong
Published: June 2024

Background

The Anopheles dirus complex plays a significant role as a malaria vector in the Greater Mekong Subregion (GMS), with varying degrees of vector competence among species. Accurate identification of sibling species in this complex is essential for understanding malaria transmission dynamics and deploying effective vector control measures. However, the original molecular identification assay, Dirus allele-specific polymerase chain reaction (AS-PCR), targeting the ITS2 region, has pronounced nonspecific amplifications leading to ambiguous results and misidentification of the sibling species. This study investigates the underlying causes of these inconsistencies and develops new primers to accurately identify species within the Anopheles dirus complex.

Methods

The AS-PCR reaction and thermal cycling conditions were modified to improve specificity for An. dirus member species identification. In silico analyses with Benchling and Primer-BLAST were conducted to identify problematic primers and design a new set for Dirus complex species identification PCR (DiCSIP). DiCSIP was then validated with laboratory and field samples of the An. dirus complex.

Results

Despite several optimizations by reducing primer concentration, decreasing thermal cycling time, and increasing annealing temperature, the Dirus AS-PCR continued to produce inaccurate identifications for Anopheles dirus, Anopheles scanloni, and Anopheles nemophilous. Subsequently, in silico analyses pinpointed problematic primers with high Guanine-Cytosine (GC) content and multiple off-target binding sites. Through a series of in silico analyses and laboratory validation, a new set of primers for Dirus complex species identification PCR (DiCSIP) has been developed. DiCSIP primers improve specificity, operational range, and sensitivity to identify five complex member species in the GMS accurately. Validation with laboratory and field An. dirus complex specimens demonstrated that DiCSIP could correctly identify all samples while the original Dirus AS-PCR misidentified An. dirus as other species when used with different thermocyclers.

Conclusions

The DiCSIP assay offers a significant improvement in An. dirus complex identification, addressing challenges in specificity and efficiency of the previous ITS2-based assay. This new primer set provides a valuable tool for accurate entomological surveys, supporting effective vector control strategies to reduce transmission and prevent malaria re-introducing in the GMS.

Graphical Abstract
PubChem synonym filtering process using crowdsourcing
Authors: Kim Sunghwan , Yu Bo , Li Qingliang , Bolton Evan E.
Published: June 2024

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical information resource containing more than 100 million unique chemical structures. One of the most requested tasks in PubChem and other chemical databases is to search chemicals by name (also commonly called a “chemical synonym”). PubChem performs this task by looking up chemical synonym-structure associations provided by individual depositors to PubChem. In addition, these synonyms are used for many purposes, including creating links between chemicals and PubMed articles (using Medical Subject Headings (MeSH) terms). However, these depositor-provided name-structure associations are subject to substantial discrepancies within and between depositors, making it difficult to unambiguously map a chemical name to a specific chemical structure. The present paper describes PubChem’s crowdsourcing-based synonym filtering strategy, which resolves inter- and intra-depositor discrepancies in synonym-structure associations as well as in the chemical-MeSH associations. The PubChem synonym filtering process was developed based on the analysis of four crowd-voting strategies, which differ in the consistency threshold value employed (60% vs 70%) and how to resolve intra-depositor discrepancies (a single vote vs. multiple votes per depositor) prior to inter-depositor crowd-voting. The agreement of voting was determined at six levels of chemical equivalency, which considers varying isotopic composition, stereochemistry, and connectivity of chemical structures and their primary components. While all four strategies showed comparable results, Strategy I (one vote per depositor with a 60% consistency threshold) resulted in the most synonyms assigned to a single chemical structure as well as the most synonym-structure associations disambiguated at the six chemical equivalency contexts. Based on the results of this study, Strategy I was implemented in PubChem’s filtering process that cleans up synonym-structure associations as well as chemical-MeSH associations. This consistency-based filtering process is designed to look for a consensus in name-structure associations but cannot attest to their correctness. As a result, it can fail to recognize correct name-structure associations (or incorrect ones), for example, when a synonym is provided by only one depositor or when many contributors are incorrect. However, this filtering process is an important starting point for quality control in name-structure associations in large chemical databases like PubChem.
Evaluation of gastrin-releasing peptide receptor, prostate-specific membrane antigen, and neurotensin receptor 1 as potential biomarkers for accurate prostate cancer stratified diagnosis
Authors: Xiao Ling , Fang Zhihui , Tang Yongxiang , Sun Yanyan , Zhu Zehua , Li Jian , Zhou Ming , Yang Nengan , Zheng Kai , Hu Shuo
Published: June 2024

Background

Studies on single-target PET imaging of gastrin-releasing peptide receptor (GRPR), prostate-specific membrane antigen (PSMA), or neurotensin receptor 1(NTR1) have been reported. However, the performance of these three targets in the progression of PCa remains unclear. Our study aims to compare the expression of GRPR, PSMA, and NTR1 in patients with prostatic intraepithelial neoplasia (PIN), prostate cancer (PCa), and lymph node metastasis. We synthesized molecular probes targeting the markers to achieve a non-invasive precise detection of PCa patients with PET/CT imaging.

Methods

In this study, the expression of GRPR, PSMA, and NTR1 was evaluated by immunohistochemistry in 34 PIN, 171 PCa, and 22 lymph node metastasis tissues of patients. The correlation between their expression and the clinicopathological parameters of PCa patients was assessed. Sixteen PCa patients with different Gleason scores (GS) underwent dual-tracer (68Ga-NOTA-RM26 and 68Ga-NOTA-PSMA617) PET/CT.

Results

In the PIN stage, the expression of GRPR was significantly higher than that of PSMA and NTR1 (P < 0.001), while NTR1 expression was significantly higher than PSMA and GRPR expression in primary PCa (P = 0.001). High PSMA expression in PCa patients was associated with shorter progression-free survival (P = 0.037) and overall survival (P = 0.035). PCa patients with high GS had higher tumor uptake of 68Ga-NOTA-PSMA617 than those with low GS (P = 0.001), while PCa patients with low GS had higher tumor uptake of 68Ga-NOTA-RM26 than those with high GS (P = 0.001).

Conclusions

This study presents three novel biomarkers (PSMA, GRPR, and NTR1) as imaging agents for PET/CT, and may offer a promising approach for non-invasive precise detection and Gleason grade prediction of PCa patients.
Analysis of Aspergillus niger isolated from ancient palm leaf manuscripts and its deterioration mechanisms
Authors: Chu Shimin , Lin Lanying , Tian Xingling
Published: June 2024

Palm leaf manuscripts (PLMs), venerable historical artefacts containing Buddhist scriptures, history, mathematics and literature, which are carried by palm leaves (Corypha umbraculifera) and are highly susceptible to microbial degradation during prolonged storage. This degradation results in significant alterations to both the appearance and material properties of PLMs, but the precise mechanism underlying this deterioration remains a mystery. To this end, the present study focused on ancient PLMs from Xishuangbanna Dai Autonomous Prefecture in Yunnan Province, China. The objective of present study was to isolate, culture and identify the microorganisms typically found in these manuscripts and to use them to biodegrade the carriers of PLMs. Detailed observations of the biodegradation behavior of these microorganisms on the carriers of PLMs were carried out, together with characterizations of the hierarchical structure and mechanical properties of the leaf fiber cell walls. This comprehensive analysis provided insights into the deterioration mechanisms of the carriers of PLMs. The study revealed the presence of the common fungus Aspergillus niger on ancient PLMs. Aspergillus niger can secrete cellulase, lipase, and acidic substances after colonizing on the carriers of PLMs. These substances sequentially damage the carrier's epidermal cells, mesophyll cells, and leaf fibers, leading to the separation of different tissue structures. At the molecular level, the lipids on the surface of the leaves were degraded initially, and sequential depolymerization of hemicellulose, amorphous cellulose, and crystalline cellulose occurred. Additionally, this study firstly applied nanoindentation technology in the research of PLMs. The mechanical properties of the cell walls underwent notable alterations due to the modifications in chemical and crystalline structure of the carriers of PLMs upon the biodegradation of Aspergillus niger. Specifically, the hardness and elastic modulus of leaf fiber cell walls showed an initial increasing and then decreasing trend, consistent with the trend of cellulose crystallinity, which also provided a new reference for assessing the degree of deterioration of PLMs.
Quantifying the expansion rates of aftershock zones for magnitude-7 class earthquakes around the Japanese archipelago
Authors: Mitsui Yuta , Utagawa Yuya , Miyamoto Ayaka
Published: June 2024

Earthquakes (mainshocks) trigger sequences of aftershocks, the frequency of which diminishes following a power-law decay, while the spatial domain of these aftershocks extends logarithmically over time. The delineation of the aftershock zone can be modulated by variables beyond the magnitude of the mainshock, encompassing the location of the fault (whether the fault is at a plate boundary), the depth at which the event occurs, and the prevailing local stress conditions. Here, we evaluate the expansion rate of aftershock zones by analyzing earthquakes of magnitude-7 class in the vicinity of the Japanese archipelago. Prior studies have offered approximate assessments of expansion rates; however, our approach involves the utilization of a straightforward algorithm for the automated estimation of this metric, facilitating the compilation of a catalog. Across the dataset, no pronounced correlations were discerned between the expansion rate and other examined parameters. Yet, an inverse relationship is identified between the expansion rate and the b value of aftershocks for mainshocks occurring at plate boundaries. This observation suggests that the expansion rate of aftershock zones predominantly mirrors the stress field following the mainshock. Such a pattern is not detected in mainshocks occurring within the plate's interior. While the expansion rate of aftershock zones is likely influenced by various factors, aftershock zones may expand more rapidly with higher differential stress in areas surrounding hypocenters of major interplate earthquakes of magnitude 8 or 9.
Causes of child labor and working conditions in Ethiopia: evidence from temporary inter-rural child labor migrants from Sekela district
Authors: Zewdie Birhanu Mengist , Berlie Arega Bazezew , Mersha Linger Ayele
Published: June 2024

Child labor migration is a critical issue that has received limited attention from academic and policy domains in many developing countries. This article analysis the causes of child labor migration and working conditions at the destination of temporary inter-rural child labor migration from Sekela district, Northwest Ethiopia. Mixed research methods is employed. Quantitative data is collected from randomly selected 204 respondents engaged in child labor migration. Focus group discussion, informal conversational interviews, and observation are also undertaken to gather qualitative data. Most child labor migrants participate in child labor migration due to localized economic constraints, which are reflected through a desire to have modern goods and cloth, food scarcity, and resource degradation. Also, the expansion of commercial farms at destination areas directly or indirectly due to the globalized market indicates that migrant children’s local labor cannot be delinked from material realities created by both national and international economic policy. The income from child labor migration supports children and their households’ livelihood, though it is at the expense of children’s education as well as exposure to worse working conditions at their destination in terms of exploitation, underpayment, malnutrition, breaching of an agreement by employers, and harassment. Hence, to exploit the benefits and minimize the risks, balanced measures should be taken at the origin and destination. Education programs should be supportive in increasing food productivity and assisting households to diversify their means of income by allowing them to participate in non-farm activities. It is also essential to the establishment of support groups in migrant destination places and the expansion of labor inspectorates who continuously supervise the safety of child labor migrants.
Speculative futures for higher education
Authors: Bayne Sian , Ross Jen
Published: June 2024

This paper uses speculative methods as a way of imagining futures for higher education in open, non-predictive ways. The complexity and ‘unknowability’ of the highly technologised, environmentally damaged and politically degraded futures we seem to be facing can mean that our conversations about the future of higher education have a tendency to spiral too quickly into dystopianism and hopelessness. Speculative methods can help open up new kinds of conversation capable of supporting active and fundamental hope. Working within a postqualitative framework, we argue that such approaches support the collaborative imagining of multiple alternatives, and represent a way of advocating for those that are preferable. The paper presents a series of speculative scenarios and microfictions focusing on worlds ruptured by climate change, artificial intelligence, revolution and the technological enhancement of humans, connecting each of these to current critical research focused on climate crisis, ‘big tech’, rising global injustice and ‘big pharma’. It emphasises the vital contribution and place of higher education within such futures, and advocates for speculative methods as an approach to maintaining hope.
Engaging for the future: challenge-based learning and stakeholder partnerships in sustainability education
Authors: Höffken Johanna , Lazendic-Galloway Jasmina
Published: June 2024

This paper aims to illustrate a mechanism and the impact of collaboration between academia and external stakeholders using a challenge-based learning (CBL) approach, with ill-defined open challenges as the starting point of the learning journey. The context of the study is a sequence of three connected courses that enabled students to address pressing sustainability challenges both in the Global South and the Global North in a variety of fields, such as health, urban living, agriculture and waste management. The courses aimed to equip students with knowledge and skills on how to tackle the challenges and design responsible innovations, as well as inspire them to make an impact in the world, while collaborating with external stakeholders. The insights gained from this study are that the courses effectively fostered critical thinking, organizational skills, and teamwork capabilities among students. The collaboration with external partners was particularly valued by the students for its relevance to their learning and potential career progression, as it allowed them to take ownership of the challenges to tackle, thereby enhancing motivation and deepening their learning experience. Furthermore, the courses were successful in enabling students to apply knowledge to real-world cases and engage in meaningful debates on responsible innovation and SDGs.
Overlooked anterior anal misplacement: a ‘forme fruste’ anomaly and potentially correctable cause of constipation
Authors: AbouZeid Amr Abdelhamid , AbdelMalek Amr Abdel Rahman , Hassan Omar Goda , Mohammad Shaimaa Abdelsattar
Published: June 2024

Purpose

To share our experience in the management of a specific type of mild anorectal anomaly presenting with severe constipation.

Methods

Data of patients with idiopathic constipation who were referred to our pediatric surgical facility during the period from 2013 to 2023 were retrospectively analyzed. The study included patients with significant symptoms of constipation associated with a minor degree of unrecognized anterior anal misplacement. MRI was ordered in most cases to confirm the presence of deeper structural abnormalities.

Results

Data from 16 cases were available for retrospective analysis. All included cases underwent posterior anoplasty with posterior sphincterotomy. Postoperative recovery was uneventful in all cases with no wound complications. Almost all cases reported obvious improvement of symptoms regarding frequency of defecation and relief of straining which was the most distressing preoperative complaint. After 1 month, 12 cases were off laxatives with regular defecation; 3 cases still required lower doses of laxatives. Only one case reported unsatisfactory response up to 3 months postoperatively, and unfortunately was lost to follow-up. Five cases responded to messages sent to them through registered phone numbers and were available for longer-term follow-up.

Conclusion

Mild unrecognized forms of anorectal anomalies may present with idiopathic constipation. Severe straining at defecation is an important clue. The subtle abnormality on perineal examination can be supported by pelvic MRI unveiling more structural abnormalities at a deeper level. Posterior anoplasty offered relief of symptoms in most cases.
Modelling COVID-19 transmission dynamics in Laos under non-pharmaceutical interventions, vaccination, and replacement of SARS-CoV-2 variants
Authors: Zhang Xu-Sheng , Luo Hong , Charlett Andre , DeAngelis Daniela , Liu Wei , Vickerman Peter , Woolhouse Mark , Wu Linxiong
Published: June 2024

Background

Understanding how the COVID-19 pandemic evolved under control measures is crucial to tackle the SARS-CoV-2 virus spread. Laos, a country bordering China but with late occurrence and low burden of COVID-19 compared to its neighbouring countries, was used for a case study.

Methods

A transmission model with disease reporting was proposed to investigate the impact of control measures on the SARS-CoV-2 virus spread in Laos from April 2021 to May 2022. It was assumed that the transmission rate changed with people’s behaviours, control measures and emerging variants; susceptibility decreased with vaccination and infection. Bayesian inference was used for model calibration to data of confirmed cases, deaths, and recoveries, and the deviance information criterion was used to select the best model variant.

Results

Our model including Non-pharmaceutical interventions (NPIs), behaviour change, vaccination, and changing variants well explained the three waves in Laos. The Alpha variant was estimated to have a basic reproduction number of 1.55 (95% CrI: 1.47–1.64) and was replaced by the Delta variant from September 2021 which was 1.88 (95% CrI: 1.77–2.01) times more transmissible; the Delta variant was replaced by Omicron variant from March 2022 which was 3.33 (95% CrI: 2.84–3.74) times more transmissible. The Delta variant was the most severe with a case fatality rate of 1.05% (95% CrI: 0.96–1.15%) while the Alpha variant and Omicron variant were much milder. The ascertainment rate was low and variable: first decreasing from 13.2 to 1.8% by 23 May 2021, and then increasing to 23.4% by 15 March 2022. Counterfactual simulations indicated that vaccination played strong roles in reducing infections even under the emergence of immune escape variants while behaviour change delayed but might not flatten the peak of outbreaks.

Conclusions

The three waves of Laos’ epidemics were due to the invasion of more transmissible and immune escape variants that affected the herd immunity built via vaccination and infection. Even with immunity waning and the escape of new variants, vaccination was still the major contributor to control COVID-19 and combining behaviour changes and vaccination would best suppress future outbreaks of COVID-19.
Bacterial–microalgal consortia for bioremediation of textile industry wastewater and resource recovery for circular economy
Authors: Rathour Ranju Kumari , Sharma Deepak , Ullah Saleem , Mahmoud El-Hassan M. , Sharma Nitish , Kumar Pradeep , Bhatt Arvind Kumar , Ahmad Irshad , Bhatia Ravi Kant
Published: June 2024

Textile industries discharge significant amounts of toxic chemicals, including residual dyes and various other xenobiotic compounds, into the environment, leading to adverse effects such as toxicity, mutagenicity, and carcinogenicity. While physico-chemical methods are commonly used for dye removal, bioremediation with microorganisms offers a greener and more eco-friendly alternative. Many microorganisms, including fungi, bacteria, and microalgae, possess the ability to degrade textile dyes through their metabolic pathways. However, their biodegradation potential is often hindered by factors such as cytotoxic effects of dyes, unfavorable environmental conditions, dye composition, concentration, and microbial types. In recent years, different strains of fungi, bacteria, and microalgae have been employed individually or in consortia for textile dye biodegradation. Nevertheless, there is a notable gap in research regarding the use of “bacterial–microalgal consortia” as a novel approach for efficient textile dye detoxification. This review aims to provide updated insights into the symbiotic interactions between bacteria and microalgae in degrading textile dyes. It discusses various technological, resource recovery, and economic challenges, as well as future prospects of this approach for textile wastewater treatment, emphasizing its potential for environmental and economic benefits.

Graphical Abstract
Linvoseltamab for Treatment of Relapsed/Refractory Multiple Myeloma
Authors: Bumma Naresh , Richter Joshua , Jagannath Sundar , Lee Hans C. , Hoffman James E. , Suvannasankha Attaya , Zonder Jeffrey A. , Shah Mansi R. , Lentzsch Suzanne , Baz Rachid , Maly Joseph J. , Namburi Swathi , Pianko Matthew J. , Ye Jing Christine , Wu Ka Lung , Silbermann Rebecca , Min Chang-Ki , Vekemans Marie-Christiane , Munder Markus , Byun Ja Min , Martínez-Lopez Joaquín , Cassady Kaniel , DeVeaux Michelle , Chokshi Dhruti , Boyapati Anita , Hazra Anasuya , Yancopoulos George D. , Sirulnik L. Andres , Rodriguez Lorenc Karen , Kroog Glenn S. , Houvras Yariv , Dhodapkar Madhav V.
Published: June 2024

PURPOSE

We present a phase I/II first-in-human trial evaluating the safety and efficacy of 50 mg and 200 mg doses of linvoseltamab, a B-cell maturation antigen × CD3 bispecific antibody in relapsed/refractory multiple myeloma (RRMM).

METHODS

Phase II eligible patients had RRMM that either progressed on/after ≥three lines of therapy including a proteasome inhibitor (PI), an immunomodulatory drug (IMiD), and an anti-CD38 antibody or was triple-class (PI/IMiD/anti-CD38) refractory. Phase II treatment was once a week through week 14 and then once every 2 weeks. Phase II 200 mg patients who achieved a ≥very good partial response by week 24 received linvoseltamab once every 4 weeks. The primary end point in phase II was overall response rate (ORR).

RESULTS

Among the 117 patients treated with 200 mg, the median age was 70 years, 39% had high-risk cytogenetics, and 28% had penta-refractory disease. At a median follow-up of 14.3 months, the ORR was 71%, with 50% achieving ≥complete response (CR). In 104 patients treated with 50 mg at a median follow-up of 7.4 months, the ORR was 48%, with 21% achieving ≥CR. The median duration of response (DOR) for 200 mg patients (n = 83) was 29.4 months (95% CI, 19.2 to not evaluable). Among 200 mg patients, the most common adverse events included cytokine release syndrome (35.0% Gr1, 10.3% Gr2, 0.9% Gr3), neutropenia (0.9% Gr2, 18.8% Gr3, 23.1% Gr4), and anemia (3.4% Gr1, 4.3% Gr2, 30.8% Gr3). Immune effector cell-associated neurotoxicity syndrome occurred in 7.7% of patients (2.6% each Gr1, Gr2, Gr3). Infections were reported in 74.4% of patients (33.3% Gr3, 2.6% Gr4); infection frequency and severity declined over time.

CONCLUSION

Linvoseltamab 200 mg induced deep and durable responses, with a median DOR of 29.4 months, in patients with RRMM with an acceptable safety profile.
IMMUnity Unveiled: A Translational NETwork for tackling PARKinson's Disease – IMMUPARKNET
Authors: Gugu Mitilda , Acharya Shubhra , Pira Dogukan , Poletti Simona , di Flora Alessia , Saksida Tamara , Pilipenko Vladimirs , Romero-Ramos Marina , Marino Franca , Muñoz Delgado Laura , Nikolovski Neda , Gursoy Ozdemir Yasemin , Lawson Dale , Comi Cristoforo , Figueira Inês
Published: June 2024

Parkinson’s disease (PD) affects more than one million people in the EU. It currently has no definitive cure, meaning that patients rely only on symptomatic treatments, which themselves are burdened by side effects. The need for advancements in both knowledge and available treatments is thus strongly felt by patients, caregivers, and health operators. This unmet need sparked the idea of orchestrating a collaborative effort via a common network – IMMUPARKNET (The role of IMMUnity in tackling PARKinson’s disease through a Translational NETwork). The IMMUPARKNET COST Action focuses on challenges in PD and its related crosstalk with immune response. Although widely recognized, the role of immunity in the onset and development of PD is still unclear. The main goal of IMMUPARKNET is to fill this knowledge gap by establishing an innovative, interdisciplinary research network and fostering exchanges of expertise among specialists from different countries and institutions. As we gather scientists and clinicians who study immunity in PD and related fields, IMMUPARKNET will establish the first nucleus of a multidisciplinary ecosystem that aims to harmonize efforts and approaches, both in research and clinical practice, to boost the development of ground-breaking treatments for PD. Through meetings, training schools, webinars, position papers, and review manuscripts, IMMUPARKNET will lead fruitful exchanges of know-how among experts in the field. The IMMUPARKNET structure revolves around 5 working groups, with a total of 157 active members from 34 different countries. Of these active members, 58.5% are young researchers, while 67.5% come from Inclusiveness Target Countries (ITC - less research-intensive COST Members; https://www.cost.eu/about/members/). IMMUPARKNET output will facilitate the improved sharing and development of research resources, straightening the road to novel treatments and identifying where existing ones can be repurposed, all, ultimately and hopefully, finding a cure for PD.
Revolutionizing the Application of Automatic Inspection System for Industrial Parts Using AI Machine Vision Technology
Authors: Wang Baojun , Lv Yulan
Published: June 2024

This paper addresses the challenges of large data management, prolonged operation times, and low detectionefficiency encountered in automatic detection systems. To overcome these issues, we propose a novel research and application method utilizing AI machine vision technology. The methodology employs the Pulse-Coupled Neural Network (PCNN) algorithm for analyzing machine control points, enhancing system reliability and detection efficiency. Furthermore, a three-stage sliding table mechanism is implemented to facilitate seamless operation restarts. Notably, our approach significantly reduces the time required for key operations, such as feeding, imaging, decision-making, on-site inspection, and re-inspection, all within 5 seconds and 1 meter distance. It supports high-precision dynamic identification, detection, and correction of errors during high-speed movement, thereby enhancing overall system performance. The experimental results demonstrate exceptional accuracy, particularly in detecting small parts measuring 28.87 mm in length and 12.36 mm in width, achieving an impressive precision of 0.04 mm. Additionally, our system boasts meticulous hardware selection, robust software stability, and high-performance capabilities, culminating in improved detection efficiency and accuracy. This research not only contributes novel ideas and results but also holds significant commercial value in industrial applications. Overall, our proposed methodology represents a noteworthy advancement in automatic inspection systems, offering superior performance and reliability compared to existing approaches.
Research and Application of Big Data Clustering Algorithm based on AI Technology in Cloud Environment
Authors: Huang Dan , Zhang Dawei
Published: June 2024

Traditional big data filling algorithms often give inaccurate results due to vulnerabilities to different data types.To solve this problem, this study presents a new big data clustering algorithm powered by AI technology in a cloud environment. The study proposes an advanced Big Data clustering algorithm that leverages AI technology in a cloud environment. It optimizes clustering based on predicted strength using parallel processing. The research focuses on optimizing the clustering algorithm based on the predicted intensity through parallel processing. Experimental results demonstrate that image clustering stability is achieved when the number of clusters exceeds 4, indicating reduced sensitivity to random factors. Although it was not possible to precisely determine the optimal number of clusters, the use of an optimization algorithm showed that at four clusters the prediction intensity reached its peak, ensuring more accurate cluster identification. Through rigorous testing, the optimal number of clusters was determined to be 4. Clustering results show that visitors characterized by certain attributes show higher interest in most columns. This algorithm makes it easier to cluster incomplete large data, improves clustering speed, and improves the accuracy of filling in missing data. Compared to existing methods, this algorithm leverages AI technology in the cloud environment to optimize clustering based on prediction intensity, providing improved accuracy and efficiency during processing in big data management.
Application of ANN in Tourism Business Development for Demand Forecasting and Management of Tourism Headcount
Authors: Zhang Hongying , Jiang Lingyun
Published: June 2024

With the rapid development of tourism, it is imperative to forecast tourism demand to maintain the long-term stable development of the tourism industry and make good planning for future tourism enterprises. The study uses the classical model of artificial neural network-BP neural network for tourism number demand prediction, given the problems of traditional BP neural networks, such as prematurity and poor convergence speed, this paper studies the iterative optimization of the algorithm of particle swarm fusion immune mechanism and finds out the optimal network parameters, to build an IAPSO-BP tourism demand prediction model. Tourist amounts from 2007 to 2017 in certain area-related data samples, the training model of iterative speed and fitting effect, and the rolling forecasting method will be used to predict the 2018-2022 years of travel. It can be seen from the convergence curve that the convergence speed of parameter optimization of the IAPSO algorithm is the fastest; the improvedIAPSO-BP network has the best training fitting effect, with a relative average error of 2.03% and an absolute average error of 4.37%, which is better than other forecasting methods. The IAPSO-BP prediction model has higher accuracy and better performance, which can provide an effective basis for the development planning of tourism enterprises and has higher practical application value.
Application of Financial Mathematical Models Combined with Root Algorithms in Finance
Authors: Zhang Yanfeng
Published: June 2024

Investors in the financial markets must deal with various hazards, for which they must create prudent investment portfolios and risk management plans. A multi-objective optimisation approach is proposed using the root algorithm to create a multi-objective root system growth model based on many clusters. An investment risk management optimisation model based on root system growth is built into the study using distributed decision-making. To create a multi-objective root algorithm-based portfolio optimisation model, the Markowitz mean-variance model and a multi-objective root algorithm are employed. Accordingto the findings, the multi-group multi-objective root system method has a real Pareto frontier solution that is more accessible, has a faster convergence rate, and has lower fitness values. The root algorithm’s solutions are workable, and the final risk values of 0.0105, 0.0082, and 0.4623 for the 2, 4, and 6 investment objectives are all in the low-risk class range. The optimal set of solutions discovered by the algorithm had better distributivity and convergence. The Hypervolume values for the multi-group multi-objective root algorithm were 5.5298 and 3.9628 for the dual-objective portfolio and the tri-objective portfolio of investment return costs, respectively. The findings of this study can guide the development of portfolio and risk management strategies.
Research on the Teaching Model of English Teaching Quality Evaluation under the Background of MOOC
Authors: Qin Xiao
Published: June 2024

In the context of informatization, online teaching is increasingly favored by people for its convenience and resource richness. Through the Open CourseWar (MOOC) platform, people can easily access learning resources and achieve efficient line length learning. However, currently MOOC online learning cannot provide targeted evaluation for the learning effectiveness of English learners, and there is relatively little research on online English quality assessment. Propose an intelligent MOOC online English teaching evaluation technology. Firstly, analyze the existing English teaching factors and construct a quality evaluation system for online English teaching using Principal Component Analysis (PCA) and expert evaluation method. Considering that the quality of English teaching is influenced by many factors, the evaluation of English teaching quality belongs to a complex nonlinear solving problem. Therefore, the advanced GA-RBF (Genetic Algorithm Radial Basis Function Neural Network, GA-RBF) model is adopted to solve the English teaching quality evaluation model. In teaching quality evaluation, BP and RBF are selected to participate in comparative testing of teaching quality evaluation. In the training loss test of multiple models, the GA-RBF model has the best convergence speed and training performance in the oral sample test, tends to converge after 270 iterations, with a loss value of 0.24. In the evaluation of English proficiency indicators, the BP model has a significant error in testing, with an error of 13 in the evaluation of reading ability scores. The error of RBF in reading ability score evaluation is 4, and the GA-RBF model performs the best. The reading ability evaluation error is 1, and the overall evaluation performance is the best. Through the above research, an intelligent method for evaluating the quality of English teaching is proposed, which will provide important technical references for MOOC online  education evaluation and English teaching improvement.