bluuebunny/crossref_metadata_2025 huggingface dataset:
Malaria cell identification using improved machine learning and modified deep learning architecture
Authors: S. Shashikiran , H. D. Sunitha
Published: April 2024

Malaria continues to be a serious problem for public health because of its occurrence in tropical and subtropical areas with inadequate healthcare systems and few resources. For prompt intervention and treatment of malaria, effective and precise diagnosis is essential. Professional pathologists examine blood smear films by hand to get a microscopic diagnosis and another way they will do a rapid antigen malaria test which produces the result of 50% accuracy. Convolutional neural network (CNN) is a type of deep learning (DL) model that has been effectively used for a variety of image recognition applications. Our suggested approach uses, improved machine learning (IML) methods like support vector machine (SVM)+principal component analysis (PCA) fit, SVM+t-distributed stochastic neighbor embedding (t-SNE) fit, and CNN architecture with an accuracy of 86.23%, 88.27%, and 97.16% accuracy respectively, to combine feature extraction, data augmentation, and modify the layers by including the SVM algorithm in the final layer of the CNN architecture. The proposed method will significantly reduce pathologists' burden by automating the identification of malaria and improving diagnosis accuracy in resourceconstrained contexts
Efficient packaging defect detection: leveraging pre-trained vision models through transfer learning
Authors: Prastiwinarti Wiwi , Delimayanti Mera Kartika , Kurniawan Hendra , Pratama Yoga Putra , Wendho Hanin , Adi Rizky
Published: April 2024

The inspection of packaging defects is a crucial aspect of maintaining the quality of industrial production, especially in the case of boxed products. This study introduces a novel approach for detecting physical defects in product packaging boxes by integrating image processing with deep learning, specifically transfer learning with two images as an input. The proposed method utilizes both top view and side view images of the packaging to determine its condition, a significant departure from the conventional single image input. Our approach incorporates 16 pre-trained model variants from EfficientNetV2, MobileNetV3, and ResNetV2 for transfer learning as feature extractors. The experimental findings demonstrate that the best model that leverages EfficientNetV2 variant achieves 100% accuracy and F1 score in terms of classification performance. However, the most optimal model in terms of classification performance and inference speed was the one that leveraged ResNetV2 variant. This model scored 95% accuracy and 95.24% F1 score, with an inference speed of 91 ms per prediction.
Review-based analysis of clustering approaches in a recommendation system
Authors: Hera Sabeena Yasmin , Amjad Mohammad
Published: April 2024

Because of the explosion in data, it is now incredibly difficult for a single person to filter through all of the information and extract what they need. As a result, information filtering algorithms are necessary to uncover meaningful information from the massive amount of data already available online. Users can benefit from recommendation systems (RSs) since they simplify the process of identifying relevant information. User ratings are incredibly significant for creating recommendations. Previously, academics relied on historical user ratings to predict future ratings, but today, consumers are paying more attention to user reviews because they contain so much relevant information about the user's decision. The proposed approach uses written testimonials to overcome the issue of doubt in the ratings' pasts. Using two data sets, we performed experimental evaluations of the proposed framework. For prediction, clustering algorithms are used with natural language processing in this strategy. It also evaluates the findings of various methods, such as the K-mean, spectral, and hierarchical clustering algorithms, and offers conclusions on which strategy is optimal for the supplied use cases. In addition, we demonstrate that the proposed technique outperforms alternatives that do not involve clustering.
Predicting rainfall runoff in Southern Nigeria using a fused hybrid deep learning ensemble
Authors: Ojugo Arnold Adimabua , Ejeh Patrick Ogholuwarami , Odiakaose Christopher Chukwufunaya , Eboka Andrew Okonji , Emordi Frances Uchechukwu
Published: April 2024

Rainfall as an environmental feat can change fast and yield significant influence in downstream hydrology known as runoff with a variety of implications such as erosion, water quality, and infrastructures. These, in turn impact the quality of life, sewage systems, agriculture, and tourism of a nation to mention a few. It chaotic, complex, and dynamic nature has necessitated studies in the quest for future direction of such runoff via prediction models. With little successes in use of knowledge driven models, many studies have now turned to data-driven models. Dataset is retrieved from Metrological Center in Lagos, Nigeria for the period 1999-2019 for the Benin-Owena River Basin. Data is split: 70% for train and 30% for test. Our study adapts a spatial-temporal profile hidden Markov trained deep neural network. Result yields a sensitivity of 0.9, specificity 0.19, accuracy of 0.74, and improvement rate of classification of 0.12. Other ensembles underperformed when compared to proposed model. The study reveals annual rainfall is an effect of variation cycle. Models will help simulate future floods and provide lead time warnings in flood management.
Los motivos de la violencia desde el discurso mediático. Un análisis de los episodios en el espacio escolar
Authors: Saez Virginia
Published: April 2024

Este artículo presenta un estudio exploratorio que analiza los motivos de los episodios de violencia en las escuelas narrados en los discursos mediáticos, en Argentina en el período 1993-2011. Dadas las características del objeto de indagación el abordaje metodológico fue cualitativo y la información se analizó en el marco del análisis socioeducativo del discurso. Entre los resultados observamos que se construye una mirada monocausal para explicar los episodios violentos y se invisibiliza el accionar de los adultos que habitan la escuela. Los motivos varías según los actores implicados (docentes, estudiantes, familiares de estudiantes) y apelan a un discurso individualizante para explicar el fenómeno. Se identifica determinados sujetos: portadores de mala conducta, consumidores de estupefacientes, con dificultades emocionales, entre otros atributos, como responsables de la violencia. Concluimos que esta forma de representar los hechos entra en juego con las dinámicas de estigmatización social. Por su originalidad, este estudio constituye un antecedente para futuras indagaciones sobre las representaciones mediáticas de las violencias en el espacio escolar.
Selective Neural Entrainment Reveals Hierarchical Tuning to Linguistic Regularities in Reading
Authors: De Rosa Mara , Vignali Lorenzo , D’Urso Anna , Ktori Maria , Bottini Roberto , Crepaldi Davide
Published: April 2024

Reading is both a visual and a linguistic task, and as such it relies on both general-purpose, visual mechanisms and more abstract, meaning-oriented processes. Disentangling the roles of these resources is of paramount importance in reading research. The present study capitalizes on the coupling of fast periodic visual stimulation and MEG recordings to address this issue and investigate the role of different kinds of visual and linguistic units in the visual word identification system. We compared strings of pseudo-characters; strings of consonants (e.g., sfcl); readable, but unattested strings (e.g., amsi); frequent, but non-meaningful chunks (e.g., idge); suffixes (e.g., ment); and words (e.g., vibe); and looked for discrimination responses with a particular focus on the ventral, occipito-temporal regions. The results revealed sensitivity to alphabetic, readable, familiar, and lexical stimuli. Interestingly, there was no discrimination between suffixes and equally frequent, but meaningless endings, thus highlighting a lack of sensitivity to semantics. Taken together, the data suggest that the visual word identification system, at least in its early processing stages, is particularly tuned to form-based regularities, most likely reflecting its reliance on general-purpose, statistical learning mechanisms that are a core feature of the visual system as implemented in the ventral stream.
Multiagency Ensemble Forecast of Wildfire Air Quality in the United States: Toward Community Consensus of Early Warning
Authors: Li Yunyao , Tong Daniel , Makkaroon Peewara , DelSole Timothy , Tang Youhua , Campbell Patrick , Baker Barry , Cohen Mark , Darmenov Anton , Ahmadov Ravan , James Eric , Hyer Edward , Xian Peng
Published: April 2024

Wildfires pose increasing risks to human health and properties in North America. Due to large uncertainties in fire emission, transport, and chemical transformation, it remains challenging to accurately predict air quality during wildfire events, hindering our collective capability to issue effective early warnings to protect public health and welfare. Here, we present a new real-time Hazardous Air Quality Ensemble System (HAQES) by leveraging various wildfire smoke forecasts from three U.S. federal agencies (NOAA, NASA, and Navy). Compared to individual models, the HAQES ensemble forecast significantly enhances forecast accuracy. To further enhance forecasting performance, a weighted ensemble forecast approach was introduced and tested. Compared to the unweighted ensemble mean, the multilinear regression weighted ensemble reduced fractional bias by 34% in the major fire regions, false alarm rate by 72%, and increased hit rate by 17%. Finally, we improved the weighted ensemble using quantile regression and weighted regression methods to enhance the forecast of extreme air quality events. The advanced weighted ensemble increased the PM2.5 exceedance hit rate by 55% compared to the ensemble mean. Our findings provide insights into the development of advanced ensemble forecast methods for wildfire air quality, offering a practical way to enhance decision-making support to protect public health.
Hemophilia B and gene therapy: a new chapter with etranacogene dezaparvovec
Authors: Anguela Xavier M. , High Katherine A.
Published: April 2024

The US Food and Drug Administration (FDA)’s authorization of etranacogene dezaparvovec (Hemgenix) is a significant milestone, constituting not only the first FDA approval of a gene therapy for hemophilia but also the first approval of a liver-targeted adeno-associated virus vector gene therapy. This review summarizes the nonclinical studies and clinical development that supported regulatory clearance. Similar to other gene therapies for single gene disorders, both the short-term safety and the phenotypic improvement were unequivocal, justifying the modest-sized safety and efficacy database, which included 57 participants across the phase 2b (3 participants) and phase 3 (54 participants) studies. The most common adverse reactions included liver enzyme elevation, headache, flu-like symptoms, infusion-related reactions, creatine kinase elevation, malaise, and fatigue; these were mostly transient. One participant had hepatocellular carcinoma on a study-mandated liver ultrasound conducted 1 year after vector infusion; molecular analysis of the resected tumor showed no evidence of vector-related insertional mutagenesis as the etiology. A remarkable 96% of participants in the phase 3 trial were able to stop factor IX (FIX) prophylaxis, with the study demonstrating noninferiority to FIX prophylaxis in terms of the primary end point, annualized bleeding rate. Key secondary end points such as the annualized infusion rate, which declined by 97%, and the plasma FIX activity level at 18 months after infusion, with least squares mean increase of 34.3 percentage points compared with baseline, were both clinically and statistically significant. The FDA’s landmark approval of Hemgenix as a pioneering treatment for hemophilia stands on the shoulders of >20 years of gene therapy clinical research and heralds a promising future for genomic medicines.
Immunoglobulin replacement vs prophylactic antibiotics for hypogammaglobulinemia secondary to hematological malignancy
Authors: McQuilten Zoe K. , Weinkove Robert , Thao Le Thi Phuong , Crispin Philip , Degelia Amber , Dendle Claire , Gilbertson Michael , Johnston Anna , Keegan Anastazia , Pepperell Dominic , Pullon Humphrey , Reynolds John , van Tonder Tina , Trotman Judith , Waters Neil , Wellard Cameron , Weston Helen , Morrissey C. Orla , Wood Erica M.
Published: April 2024

Immunoglobulin replacement and prophylactic antibiotics are commonly used to prevent infections in patients with secondary hypogammaglobulinemia due to hematological malignancies but have never been directly compared. In this randomized controlled feasibility trial conducted in 7 hospitals in Australia and New Zealand, we enrolled patients with secondary hypogammaglobulinemia with either a history of recurrent/severe infection or an immunoglobulin G level <4 g/L. Participants were randomized in a 1:2 ratio to immunoglobulin (0.4 g/kg per 4 weeks IV) or daily antibiotics (trimethoprim-sulfamethoxazole 160 mg/800 mg or, if contraindicated, 100 mg doxycycline) for 12 months. Participants allocated to antibiotics were allowed to crossover after grade ≥3 infections. The primary outcome was proportion of patients alive on the assigned treatment 12 months after randomization. Between August 2017 and April 2019, 63 patients were randomized: 42 to antibiotics and 21 to immunoglobulin. Proportion of participants alive on allocated treatment at 12 months was 76% in the immunoglobulin and 71% in the antibiotic arm (Fisher exact test P=.77; odds ratio, 0.78; 95% CI, 0.22-2.52). The lower quartile for time to first major infection (median, not reached) was 11.1 months for the immunoglobulin and 9.7 months for the antibiotic arm (log-rank test, P=.65). Three participants in the immunoglobulin and 2 in the antibiotic arm had grade ≥3 treatment-related adverse events. A similar proportion of participants remained on antibiotic prophylaxis at 12 months to those on immunoglobulin, with similar rates of major infections. Our findings support the feasibility of progressing to a phase 3 trial. Trial registration #ACTRN12616001723471.
Analysis of influencing factors and construction of prediction model for postoperative nausea and vomiting in patients undergoing laparoscopic sleeve gastrectomy: a single-center retrospective cohort study
Authors: Liao Bucheng , Liao Wuhao , Wu Xinhai , Liu Shujuan , Li Yanze , Qin Ruixia , Yin Shuang
Published: April 2024

Background

With the increasing number of bariatric surgeries, the high incidence of postoperative nausea and vomiting (PONV) associated with this surgery has also gradually attracted attention. Among the common bariatric surgery methods, patients undergoing sleeve gastrectomy (SG) have the highest incidence of nausea and vomiting. The mechanism of occurrence of PONV is very complex. This study aims to explore the influencing factors of PONV in patients undergoing laparoscopic sleeve gastrectomy (LSG) and construct a nomogram prediction model based on these factors.

Methods

With the approval of the Ethics Committee, the electronic medical records of patients who underwent LSG from July 2022 to May 2023 were collected retrospectively.

Results

A total of 114 patients with complete medical records who underwent LSG from July 2022 to May 2023 were included in this study. Among them, 46 patients developed PONV, resulting in a PONV incidence rate of 40.4%. Multivariate logistic regression analysis revealed that female gender, the use of inhalation anesthesia, and operation time ≥ 120 min were risk factors for PONV in LSG. Additionally, the use of more than two kinds of antiemetic drugs was identified as a protective factor. Based on these factors, a nomogram model was constructed.

Conclusion

PONV in patients undergoing LSG is related to gender, type of anesthesia, duration of surgery, and combination therapy with antiemetic drugs. The nomogram prediction model constructed in this study demonstrates high accuracy and discrimination in predicting the occurrence of PONV in patients undergoing LSG.
A study of hospitalized COVID-19 patients with AKI in a setting of multiracial developing country
Authors: Ooi S. H. , Ng K. P. , Sthaneshwar Pavai , Lim S. K. , Khor P. Y. , Lim J. Y. , Siow W. S. , Lim K. W. , Azlan Muhummad
Published: April 2024

Background

The commonest indication for hospitalization in COVID-19 patients is hypoxemia or severe respiratory symptoms. However, COVID-19 disease may result in extrapulmonary complications including kidney-related pathology. The reported incidence of renal involvement related to COVID infection varies based on geographical location.

Objective

This study aimed to assess the incidence rate of AKI in hospitalized COVID-19 patients and identify risk factors and prognostic predictors.

Method

In this retrospective study, we recruited hospitalized COVID-19 patients from January 2021 until June 2021 at the University Malaya Medical Center. The inclusion criteria were hospitalized for ≥ 48 h with confirmed COVID-19 infection and at least 18 years old. Patient demographic and clinical data were collected from electronic medical records. The staging of AKI was based on criteria as per KDIGO guidelines.

Results

One thousand five hundred twenty-nine COVID patients fulfilled the inclusion criteria with a male-to-female ratio of 759 (49.6%) to 770 (50.3%). The median age was 55 (IQR: 36–66). 500 patients (32.7%) had diabetes, 621 (40.6%) had hypertension, and 5.6% (n = 85) had pre-existing chronic kidney disease (CKD). The incidence rate of AKI was 21.1% (n = 323). The percentage of COVID patients in different AKI stages of 1,2 and 3 were 16.3%, 2.1%, and 2.7%, respectively. Fifteen hospitalized patients (0.98%) required renal replacement therapy. 58.8% (n = 190) of AKI group had complete recovery of kidney function.

Demographic factors included age (p < 0.001), diabetes (p < 0.001), hypertension (p < 0.012), CKD (p < 0.001), and vaccination status (p = 0.042) were associated with an increased risk of developing AKI. We found that the AKI cohort had statistically significant lower platelet counts and higher ferritin levels than the non-AKI cohort. AKI is a risk predictor of prolonged hospitalization (p < 0.001) and higher mortality rates (P < 0.001).

Conclusion

AKI is a common clinical complication among hospitalized COVID-19 patients. The etiology of AKI is multifactorial and may have an adverse impact on patient morbidity and mortality.
Investigating genetic links between blood metabolites and preeclampsia
Authors: Lin Qiannan , Li Siyu , Wang Huiyan , Zhou Wenbo
Published: April 2024

Background

Observational studies have revealed that metabolic disorders are closely related to the development of preeclampsia (PE). However, there is still a research gap on the causal role of metabolites in promoting or preventing PE. We aimed to systematically explore the causal association between circulating metabolites and PE.

Methods

Single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) of 486 blood metabolites (7,824 participants) were extracted as instrumental variables (P < 1 × 10− 5), GWAS summary statistics for PE were obtained from FinnGen consortium (7,212 cases and 194,266 controls) as outcome, and a two-sample Mendelian randomization (MR) analysis was conducted. Inverse variance weighted (IVW) was set as the primary method, with MR–Egger and weighted median as auxiliary methods; the instrumental variable strength and confounding factors were also assessed. Sensitivity analyses including MR-Egger, Cochran’s Q test, MR-PRESSO and leave-one-out analysis were performed to test the robustness of the MR results. For significant associations, repeated MR and meta-analysis were performed by another metabolite GWAS (8,299 participants). Furthermore, significantly associated metabolites were subjected to a metabolic pathway analysis.

Results

The instrumental variables for the metabolites ranged from 3 to 493. Primary analysis revealed a total of 12 known (e.g., phenol sulfate, citrulline, lactate and gamma-glutamylglutamine) and 11 unknown metabolites were associated with PE. Heterogeneity and pleiotropy tests verified the robustness of the MR results. Validation with another metabolite GWAS dataset revealed consistency trends in 6 of the known metabolites with preliminary analysis, particularly the finding that genetic susceptibility to low levels of arachidonate (20:4n6) and citrulline were risk factors for PE. The pathway analysis revealed glycolysis/gluconeogenesis and arginine biosynthesis involved in the pathogenesis of PE.

Conclusions

This study identifies a causal relationship between some circulating metabolites and PE. Our study presented new perspectives on the pathogenesis of PE by integrating metabolomics with genomics, which opens up avenues for more accurate understanding and management of the disease, providing new potential candidate metabolic molecular markers for the prevention, diagnosis and treatment of PE. Considering the limitations of MR studies, further research is needed to confirm the causality and underlying mechanisms of these findings.
Effects of garlic (Allium sativum L) and Citrullus colocynthis (L.) Schrad individually and in combination on male reproductive damage due to diabetes: suppression of the AGEs/RAGE/Nox-4 signaling pathway
Authors: Mohammadzadeh Aghileh , Gol Ali , Kheirandish Reza
Published: April 2024

Background

Diabetes Mellitus is associated with disturbances in male reproductive function and fertility. Studies have shown that oxidative stress with the subsequent inflammation and apoptosis cause these complications in diabetes. Garlic (G) (Allium sativum L) and Citrullus colocynthis (L.) Schrad (C) both have antidiabetic and antioxidant properties. Recently, we demonstrated their synergistic effects in alleviating reproductive complications when administered concomitantly. However, as even medicinal plants in long term usage may lead to some unwanted side effects of their own, we examined whether with half the original doses of these two medicinal plants we could achieve the desired results.

Methods

Thirty-five male Wistar rats were divided into five groups (n = 7/group): Control, Diabetic, Diabetic + G (0.5 ml/100 g BW), Diabetic + C (5 mg/kg BW) and Diabetic + GC (0.5 ml/100 g BW of garlic and 5 mg/kg BW of C. colocynthis) groups. The experimental period was 30 days.

Results

Oxidative stress, advanced glycation end products (AGEs), immunoexpression of caspase-3, and expression of mRNAs for receptor for advanced glycation end products (RAGE), NADPH oxidase-4 (NOX-4) and nuclear factor kappa B increased in testis of diabetic rats. Treatment with garlic and C. colocynthis alone showed some beneficial effects, but in the combination form the effectiveness was more profound.

Conclusions

We conclude that the combination therapy of diabetic rats with lower doses is still as efficient as higher doses; therefore, the way forward for reducing complications in long term consumption.
Use of non-small cell lung cancer multicellular tumor spheroids to study the impact of chemotherapy
Authors: Hulo Pauline , Deshayes Sophie , Fresquet Judith , Chéné Anne-Laure , Blandin Stéphanie , Boisgerault Nicolas , Fonteneau Jean-François , Treps Lucas , Denis Marc G , Bennouna Jaafar , Fradin Delphine , Pons-Tostivint Elvire , Blanquart Christophe
Published: April 2024

Background

Lung cancers represent the main cause of cancer related-death worldwide. Recently, immunotherapy alone or in combination with chemotherapy has deeply impacted the therapeutic care leading to an improved overall survival. However, relapse will finally occur, with no efficient second line treatment so far. New therapies development based on the comprehension of resistance mechanisms is necessary. However, the difficulties to obtain tumor samples before and after first line treatment hamper to clearly understand the consequence of these molecules on tumor cells and also to identify adapted second line therapies.

Methods

To overcome this difficulty, we developed multicellular tumor spheroids (MCTS) using characterized Non-Small Cell Lung Cancer (NSCLC) cell lines, monocytes from healthy donors and fibroblasts. MCTS were treated with carboplatin-paclitaxel or -gemcitabine combinations according to clinical administration schedules. The treatments impact was studied using cell viability assay, histological analyses, 3’RNA sequencing, real-time PCR, flow cytometry and confocal microscopy.

Results

We showed that treatments induced a decrease in cell viability and strong modifications in the transcriptomic profile notably at the level of pathways involved in DNA damage repair and cell cycle. Interestingly, we also observed a modification of genes expression considered as hallmarks of response to immune check point inhibitors and immunogenicity, particularly an increase in CD274 gene expression, coding for PD-L1. This result was validated at the protein level and shown to be restricted to tumor cells on MCTS containing fibroblasts and macrophages. This increase was also observed in an additional cell line, expressing low basal CD274 level.

Conclusions

This study shows that MCTS are interesting models to study the impact of first line therapies using conditions close to clinical practice and also to identify more adapted second line or concomitant therapies for lung cancer treatment.
Comprehensive analyses of the cancer-associated fibroblast subtypes and their score system for prediction of outcomes and immunosuppressive microenvironment in prostate cancer
Authors: Gao Ze , Zhang Ning , An Bingzheng , Li Dawei , Fang Zhiqing , Xu Dawei
Published: April 2024

Background

Cancer-associated fibroblasts (CAFs) drive cancer progression and treatment failure on one hand, while their tumor-restraining functions are also observed on the other. Recent single cell RNA sequencing (scRNA-seq) analyses demonstrates heterogeneity of CAFs and defines molecular subtypes of CAFs, which help explain their different functions. However, it remains unclear whether these CAF subtypes have the same or different biological/clinical implications in prostate cancer (PCa) or other malignancies.

Methods

PCa cells were incubated with supernatant from normal fibroblasts and CAFs to assess their effects on cell behaviors. Sequencing, genomic, and clinical data were collected from TCGA, MSKCC, CPGEA and GEO databases. CAF molecular subtypes and total CAF scores were constructed and grouped into low and high groups based on CAF-specific gene expression. Progression free interval (PFI), clinicopathological features, telomere length, immune cell infiltration, drug treatment and somatic mutations were compared among CAF molecular subtypes and low/high score groups.

Results

The PCa CAF-derived supernatant promoted PCa cell proliferation and invasion. Based on differentially expressed genes identified by scRNA-seq analyses, we classified CAFs into 6 molecular subtypes in PCa tumors, and each subtype was then categorized into score-high and low groups according to the subtype-specific gene expression level. Such score models in 6 CAF subtypes all predicted PFI. Telomeres were significantly shorter in high-score tumors. The total CAF score from 6 CAF subtypes was also associated with PFI in PCa patients inversely, which was consistent with results from cellular experiments. Immunosuppressive microenvironment occurred more frequently in tumors with a high CAF score, which was characterized by increased CTLA4 expression and indicated better responses to CTLA4 inhibitors. Moreover, this model can also serve as a useful PFI predictor in pan-cancers.

Conclusion

By combining scRNA-seq and bulk RNA-seq data analyses, we develop a CAF subtype score system as a prognostic factor for PCa and other cancer types. This model system also helps distinguish different immune-suppressive mechanisms in PCa, suggesting its implications in predicting response to immunotherapy. Thus, the present findings should contribute to personalized PCa intervention.
Exposure to ambient air pollution and cognitive function: an analysis of the English Longitudinal Study of Ageing cohort
Authors: Wood Dylan , Evangelopoulos Dimitris , Beevers Sean , Kitwiroon Nutthida , Demakakos Panayotes , Katsouyanni Klea
Published: April 2024

Background

An increasing number of studies suggest adverse effects of exposure to ambient air pollution on cognitive function, but the evidence is still limited. We investigated the associations between long-term exposure to air pollutants and cognitive function in the English Longitudinal Study of Ageing (ELSA) cohort of older adults.

Methods

Our sample included 8,883 individuals from ELSA, based on a nationally representative study of people aged ≥ 50 years, followed-up from 2002 until 2017. Exposure to air pollutants was modelled by the CMAQ-urban dispersion model and assigned to the participants’ residential postcodes. Cognitive test scores of memory and executive function were collected biennially. The associations between these cognitive measures and exposure to ambient concentrations of NO2, PM10, PM2.5 and ozone were investigated using mixed-effects models adjusted for time-varying age, physical activity and smoking status, as well as baseline gender and level of education.

Results

Increasing long-term exposure per interquartile range (IQR) of NO2 (IQR: 13.05 μg/m3), PM10 (IQR: 3.35 μg/m3) and PM2.5 (IQR: 2.7 μg/m3) were associated with decreases in test scores of composite memory by -0.10 (95% confidence interval [CI]: -0.14, -0.07), -0.02 [-0.04, -0.01] and -0.08 [-0.11, -0.05], respectively. The same increases in NO2, PM10 and PM2.5 were associated with decreases in executive function score of -0.31 [-0.38, -0.23], -0.05 [-0.08, -0.02] and -0.16 [-0.22, -0.10], respectively. The association with ozone was inverse across both tests. Similar results were reported for the London-dwelling sub-sample of participants.

Conclusions

The present study was based on a long follow-up with several repeated measurements per cohort participant and long-term air pollution exposure assessment at a fine spatial scale. Increasing long-term exposure to NO2, PM10 and PM2.5 was associated with a decrease in cognitive function in older adults in England. This evidence can inform policies related to modifiable environmental exposures linked to cognitive decline.
Inhibition of adenylyl cyclase by GTPase-deficient Gαi is mechanistically different from that mediated by receptor-activated Gαi
Authors: Chung Yin Kwan , Chan Ho Yung , Lee Tung Yeung , Wong Yung Hou
Published: April 2024

Signal transduction through G protein-coupled receptors (GPCRs) has been a major focus in cell biology for decades. Numerous disorders are associated with GPCRs that utilize Gi proteins to inhibit adenylyl cyclase (AC) as well as regulate other effectors. Several early studies have successfully defined the AC-interacting domains of several members of Gαi by measuring the loss of activity upon homologous replacements of putative regions of constitutive active Gαi mutants. However, whether such findings can indeed be translated into the context of a receptor-activated Gαi have not been rigorously verified. To address this issue, an array of known and new chimeric mutations was introduced into GTPase-deficient Q204L (QL) and R178C (RC) mutants of Gαi1, followed by examinations on their ability to inhibit AC. Surprisingly, most chimeras failed to abolish the constitutive activity brought on by the QL mutation, while some were able to eliminate the inhibitory activity of RC mutants. Receptor-mediated inhibition of AC was similarly observed in the same chimeric constructs harbouring the pertussis toxin (PTX)-resistant C351I mutation. Moreover, RC-bearing loss-of-function chimeras appeared to be hyper-deactivated by endogenous RGS protein. Molecular docking revealed a potential interaction between AC and the α3/β5 loop of Gαi1. Subsequent cAMP assays support a cooperative action of the α3/β5 loop, the α4 helix, and the α4/β6 loop in mediating AC inhibition by Gαi1-i3. Our results unveiled a notable functional divergence between constitutively active mutants and receptor-activated Gαi1 to inhibit AC, and identified a previously unknown AC-interacting domain of Gαi subunits. These results collectively provide valuable insights on the mechanism of AC inhibition in the cellular environment.
Green chemometric-assisted UV-spectrophotometric methods for the determination of favipiravir, cefixime and moxifloxacin hydrochloride as an effective therapeutic combination for COVID-19; application in pharmaceutical form and spiked human plasma
Authors: Madbouly Eman A. , El-Shanawani Abdalla A. , El-adl Sobhy M. , Abdelkhalek Ahmed S.
Published: April 2024

As pharmaceutical analysis progresses towards environmental sustainability, there is a growing need to enhance the safety and health conditions for analysts. Consequently, the incorporation of chemometrics into environmentally friendly analytical methods represents a promising approach. Favipiravir, cefixime, and moxifloxacin hydrochloride have been currently used in COVID-19 treatment. In this study, we develop spectrophotometric methods depending on chemometric based models to measure the levels of favipiravir, cefixime, and moxifloxacin hydrochloride in pharmaceutical preparations and spiked human plasma. It is challenging to determine favipiravir, cefixime, and moxifloxacin simultaneously because of overlap in their UV absorption spectra. Two advanced chemometric models, partial least square (PLS) and genetic algorithm (GA), have been developed to provide better predictive abilities in spectrophotometric determination of the drugs under study. The described models were created using a five-level, three-factor experimental design. The outcomes of the models have been thoroughly assessed and interpreted, and a statistical comparison with recognized values has been taken into consideration. The analytical eco-scale and the green analytical procedure index (GAPI) evaluation methods were also utilized to determine how environmentally friendly the mentioned models were. The outcomes demonstrated how well the models described complied with the environmental requirements.
The value of machine learning based on CT radiomics in the preoperative identification of peripheral nerve invasion in colorectal cancer: a two-center study
Authors: Liu Nian-jun , Liu Mao-sen , Tian Wei , Zhai Ya-nan , Lv Wei-long , Wang Tong , Guo Shun-Lin
Published: April 2024

Background

We aimed to explore the application value of various machine learning (ML) algorithms based on multicenter CT radiomics in identifying peripheral nerve invasion (PNI) of colorectal cancer (CRC).

Methods

A total of 268 patients with colorectal cancer who underwent CT examination in two hospitals from January 2016 to December 2022 were considered. Imaging and clinicopathological data were collected through the Picture Archiving and Communication System (PACS). The Feature Explorer software (FAE) was used to identify the peripheral nerve invasion of colorectal patients in center 1, and the best feature selection and classification channels were selected. Finally, the best feature selection and classifier pipeline were verified in center 2.

Results

The six-feature models using RFE feature selection and GP classifier had the highest AUC values, which were 0.610, 0.699, and 0.640, respectively. FAE generated a more concise model based on one feature (wavelet-HLL-glszm-LargeAreaHighGrayLevelEmphasis) and achieved AUC values of 0.614 and 0.663 on the validation and test sets, respectively, using the “one standard error” rule. Using ANOVA feature selection, the GP classifier had the best AUC value in a one-feature model, with AUC values of 0.611, 0.663, and 0.643 on the validation, internal test, and external test sets, respectively. Similarly, when using the “one standard error” rule, the model based on one feature (wave-let-HLL-glszm-LargeAreaHighGrayLevelEmphasis) achieved AUC values of 0.614 and 0.663 on the validation and test sets, respectively.

Conclusions

Combining artificial intelligence and radiomics features is a promising approach for identifying peripheral nerve invasion in colorectal cancer. This innovative technique holds significant potential for clinical medicine, offering broader application prospects in the field.

Critical relevance statement

The multi-channel ML method based on CT radiomics has a simple operation process and can be used to assist in the clinical screening of patients with CRC accompanied by PNI.

Key points

• Multi-channel ML in the identification of peripheral nerve invasion in CRC.

• Multi-channel ML method based on CT-radiomics can detect the PNI of CRC.

• Early preoperative identification of PNI in CRC is helpful to improve the formulation of treatment strategies and the prognosis of patients.

Graphical Abstract
How to differentiate induced pluripotent stem cells into sensory neurons for disease modelling: a functional assessment
Authors: Kalia Anil Kumar , Rösseler Corinna , Granja-Vazquez Rafael , Ahmad Ayesha , Pancrazio Joseph J. , Neureiter Anika , Zhang Mei , Sauter Daniel , Vetter Irina , Andersson Asa , Dussor Gregory , Price Theodore J. , Kolber Benedict J. , Truong Vincent , Walsh Patrick , Lampert Angelika
Published: April 2024

Background

Human induced pluripotent stem cell (iPSC)-derived peripheral sensory neurons present a valuable tool to model human diseases and are a source for applications in drug discovery and regenerative medicine. Clinically, peripheral sensory neuropathies can result in maladies ranging from a complete loss of pain to severe painful neuropathic disorders. Sensory neurons are located in the dorsal root ganglion and are comprised of functionally diverse neuronal types. Low efficiency, reproducibility concerns, variations arising due to genetic factors and time needed to generate functionally mature neuronal populations from iPSCs remain key challenges to study human nociception in vitro. Here, we report a detailed functional characterization of iPSC-derived sensory neurons with an accelerated differentiation protocol (“Anatomic” protocol) compared to the most commonly used small molecule approach (“Chambers” protocol). Anatomic’s commercially available RealDRG™ were further characterized for both functional and expression phenotyping of key nociceptor markers.

Methods

Multiple iPSC clones derived from different reprogramming methods, genetics, age, and somatic cell sources were used to generate sensory neurons. Manual patch clamp was used to functionally characterize both control and patient-derived neurons. High throughput techniques were further used to demonstrate that RealDRGs™ derived from the Anatomic protocol are amenable to high throughput technologies for disease modelling.

Results

The Anatomic protocol rendered a purer culture without the use of mitomycin C to suppress non-neuronal outgrowth, while Chambers differentiations yielded a mix of cell types. Chambers protocol results in predominantly tonic firing when compared to Anatomic protocol. Patient-derived nociceptors displayed higher frequency firing compared to control subject with both, Chambers and Anatomic differentiation approaches, underlining their potential use for clinical phenotyping as a disease-in-a-dish model. RealDRG™ sensory neurons show heterogeneity of nociceptive markers indicating that the cells may be useful as a humanized model system for translational studies.

Conclusions

We validated the efficiency of two differentiation protocols and their potential application for functional assessment and thus understanding the disease mechanisms from patients suffering from pain disorders. We propose that both differentiation methods can be further exploited for understanding mechanisms and development of novel treatments in pain disorders.
Mesenchymal stromal cell chondrogenesis under ALK1/2/3-specific BMP inhibition: a revision of the prohypertrophic signalling network concept
Authors: Diederichs Solvig , Dreher Simon I. , Nüesch Sarah Anna , Schmidt Sven , Merle Christian , Richter Wiltrud
Published: April 2024

Background

In vitro chondrogenesis of mesenchymal stromal cells (MSCs) driven by the essential chondro-inducer transforming growth factor (TGF)-β is instable and yields undesired hypertrophic cartilage predisposed to bone formation in vivo. TGF-β can non-canonically activate bone morphogenetic protein-associated ALK1/2/3 receptors. These have been accused of driving hypertrophic MSC misdifferentiation, but data remained conflicting. We here tested the antihypertrophic capacity of two highly specific ALK1/2/3 inhibitors – compound A (CompA) and LDN-212854 (LDN21) – in order to reveal potential prohypertrophic contributions of these BMP/non-canonical TGF-β receptors during MSC in vitro chondrogenesis.

Methods

Standard chondrogenic pellet cultures of human bone marrow-derived MSCs were treated with TGF-β and CompA (500 nM) or LDN21 (500 nM). Daily 6-hour pulses of parathyroid hormone-related peptide (PTHrP[1–34], 2.5 nM, from day 7) served as potent antihypertrophic control treatment. Day 28 samples were subcutaneously implanted into immunodeficient mice.

Results

All groups underwent strong chondrogenesis, but GAG/DNA deposition and ACAN expression were slightly but significantly reduced by ALK inhibition compared to solvent controls along with a mild decrease of the hypertrophy markers IHH-, SPP1-mRNA, and Alkaline phosphatase (ALP) activity. When corrected for the degree of chondrogenesis (COL2A1 expression), only pulsed PTHrP but not ALK1/2/3 inhibition qualified as antihypertrophic treatment. In vivo, all subcutaneous cartilaginous implants mineralized within 8 weeks, but PTHrP pretreated samples formed less bone and attracted significantly less haematopoietic marrow than ALK1/2/3 inhibitor groups.

Conclusions

Overall, our data show that BMP-ALK1/2/3 inhibition cannot program mesenchymal stromal cells toward stable chondrogenesis. BMP-ALK1/2/3 signalling is no driver of hypertrophic MSC misdifferentiation and BMP receptor induction is not an adverse prohypertrophic side effect of TGF-β that leads to endochondral MSC misdifferentiation. Instead, the prohypertrophic network comprises misregulated PTHrP/hedgehog signalling and WNT activity, and a potential contribution of TGF-β-ALK4/5-mediated SMAD1/5/9 signalling should be further investigated to decide about its postulated prohypertrophic activity. This will help to successfully engineer cartilage replacement tissues from MSCs in vitro and translate these into clinical cartilage regenerative therapies.
Identification of fibronectin type III domain containing 3B as a potential prognostic and therapeutic target for pancreatic cancer: a preliminary analysis
Authors: Wang Yizhi , Kong Yang , Yang Qifan , Zhong Cheng , Zhou Dongkai
Published: April 2024

Background

Fibronectin type III domain containing 3B (FNDC3B), a member of the fibronectin type III domain-containing protein family, has been indicated in various malignancies. However, the precise role of FNDC3B in the progression of pancreatic cancer (PC) still remains to be elucidated.

Methods

In this study, we integrated data from the National Center for Biotechnology Information, the Cancer Genome Atlas, Genotype-Tissue Expression database, and Gene Expression Omnibus datasets to analyze FNDC3B expression and its association with various clinicopathological parameters. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, along with Gene Set Enrichment Analysis (GSEA), single sample Gene Set Enrichment Analysis (ssGSEA) and estimate analysis were recruited to delve into the biological function and immune infiltration based on FNDC3B expression. Additionally, the prognostic estimation was conducted using Cox analysis and Kaplan–Meier analysis. Subsequently, a nomogram was constructed according to the result of Cox analysis to enhance the prognostic ability of FNDC3B. Finally, the preliminary biological function of FNDC3B in PC cells was explored.

Results

The study demonstrated a significantly higher expression of FNDC3B in tumor tissues compared to normal pancreatic tissues, and this expression was significantly associated with various clinicopathological parameters. GSEA revealed the involvement of FNDC3B in biological processes and signaling pathways related to integrin signaling pathway and cell adhesion. Additionally, ssGSEA analysis indicated a positive correlation between FNDC3B expression and infiltration of Th2 cells and neutrophils, while showing a negative correlation with plasmacytoid dendritic cells and Th17 cells infiltration. Kaplan–Meier analysis further supported that high FNDC3B expression in PC patients was linked to shorter overall survival, disease-specific survival, and progression-free interval. However, although univariate analysis demonstrated a significant correlation between FNDC3B expression and prognosis in PC patients, this association did not hold true in multivariate analysis. Finally, our findings highlight the crucial role of FNDC3B expression in regulating proliferation, migration, and invasion abilities of PC cells.

Conclusion

Despite limitations, the findings of this study underscored the potential of FNDC3B as a prognostic biomarker and its pivotal role in driving the progression of PC, particularly in orchestrating immune responses.
Tactile sensor-less fingertip contact detection and force estimation for stable grasping with an under-actuated hand
Authors: Doan Ha Thang Long , Arita Hikaru , Tahara Kenji
Published: April 2024

Detecting contact when fingers are approaching an object and estimating the magnitude of the force the fingers are exerting on the object after contact are important tasks for a multi-fingered robotic hand to stably grasp objects. However, for a linkage-based under-actuated robotic hand with a self-locking mechanism to realize stable grasping without using external sensors, such tasks are difficult to perform when only analyzing the robot model or only applying data-driven methods. Therefore, in this paper, a hybrid of previous approaches is used to find a solution for realizing stable grasping with an under-actuated hand. First, data from the internal sensors of a robotic hand are collected during its operation. Subsequently, using the robot model to analyze the collected data, the differences between the model and real data are explained. From the analysis, novel data-driven-based algorithms, which can overcome noted challenges to detect contact between a fingertip and the object and estimate the fingertip forces in real-time, are introduced. The proposed methods are finally used in a stable grasp controller to control a triple-fingered under-actuated robotic hand to perform stable grasping. The results of the experiments are analyzed to show that the proposed algorithms work well for this task and can be further developed to be used for other future dexterous manipulation tasks.