bluuebunny/crossref_metadata_2025 huggingface dataset:
Targeting T-cell metabolism to boost immune checkpoint inhibitor therapy
Authors: Li Haohao , Zhao Alison , Li Menghua , Shi Lizhi , Han Qiuju , Hou Zhaohua
Published: December 2022

Immune checkpoint inhibitors (ICIs) have shown promising therapeutic effects in the treatment of advanced solid cancers, but their overall response rate is still very low for certain tumor subtypes, limiting their clinical scope. Moreover, the high incidence of drug resistance (including primary and acquired) and adverse effects pose significant challenges to the utilization of these therapies in the clinic. ICIs enhance T cell activation and reverse T cell exhaustion, which is a complex and multifactorial process suggesting that the regulatory mechanisms of ICI therapy are highly heterogeneous. Recently, metabolic reprogramming has emerged as a novel means of reversing T-cell exhaustion in the tumor microenvironment; there is increasing evidence that T cell metabolic disruption limits the therapeutic effect of ICIs. This review focuses on the crosstalk between T-cell metabolic reprogramming and ICI therapeutic efficacy, and summarizes recent strategies to improve drug tolerance and enhance anti-tumor effects by targeting T-cell metabolism alongside ICI therapy. The identification of potential targets for altering T-cell metabolism can significantly contribute to the development of methods to predict therapeutic responsiveness in patients receiving ICI therapy, which are currently unknown but would be of great clinical significance.
In vitro assessment of antimicrobial, anti-inflammatory, and schistolarvicidal activity of macroalgae-based gold nanoparticles
Authors: Kamal Marwa , Abdel-Raouf Neveen , Sonbol Hana , Abdel-Tawab Heba , Abdelhameed Mohamed Sayed , Hammouda Ola , Elsayed Khaled N. M.
Published: December 2022

There is a growing need to improve facile, eco-friendly, and cheap approaches for nanoparticle (NP) synthesis. Green protocols have been investigated for the fabrication of NPs using several natural sources as plants, algae, fungi, and bacteria. Thus, the present study proposed a rapid, convenient, and efficient biosynthesis of gold NPs (Au-NPs) using the ethanolic extracts of three macroalgae, i.e., Cystoseira myrica, C. trinodis, and Caulerpa prolifera. The reduction of Au ions and the fabrication of Au-NPs were validated using ultraviolet-visible (UV–Vis) spectroscopy, X-ray diffraction, Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), and zeta potential analysis. The produced Au-NPs were tested for their antibacterial, antifungal, anti-inflammatory, and schistolarvicidal activity. Results revealed the formation of Au-NPs with an average size of 12.6–15.5 nm and different shapes that are mainly spherical with pure crystalline nature. The strong antibacterial activities of C. trinodis– and C. myrica–based Au-NPs against E. coli (inhibition zones of 22 and 19 mm) and against Staphylococcus aureus (inhibition zones of 18 and 20.5 and mm) were recorded, respectively. On the other hand, the high antifungal activity of C. trinodis Au-NPs against Aspergillus niger and Alternaria alternate showed the inhibition zones of 18 and 17 mm, respectively. The high antifungal activity of C. trinodis Au-NPs against Candida albicans (inhibition zone 16 mm) was also recorded. Regarding anti-inflammatory and schistolarvicidal activity, Au-NPs fabricated using C. myrica showed 64.2% of the inhibitory effect on protein denaturation and recorded the highest schistolarvicidal activity against Schistosoma mansoni cercariae that sank and died after 7 min. Overall, these findings proved that macroalgal ethanolic extracts can be effectively used for the biosynthesis of Au-NPs. These Au-NPs offer a significant alternative antimicrobial, anti-inflammatory, and schistolarvicidal agents. for biomedical uses.
Differentially enriched fungal communities in root rot resistant and susceptible varieties of tobacco (Nicotiana tabacum L.) under continuous monoculture cropping
Authors: Ao Jincheng , Wang Zheng , Yang Qigang , Li Bo , Li Ying , Li Yongmei
Published: December 2022

Root rot is a major disease of tobacco that causes crop losses of up to 15–20% of global tobacco production. The present study aimed to compare the fungal communities, and physicochemical properties of rhizosphere soil of root rot resistant (Yunyan 87; Y) and susceptible (Honghua Dajinyuan; H) tobacco varieties. Four treatments of each variety under continuous monocropping cultures included: control groups (HT0 and YT0); 2 years of continuous cropping (HT2 and YT2); 4 years of continuous cropping (HT4 and YT4); and 8 years of continuous cropping (YT8 and HT8). The soil physicochemical properties including available nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) were increased (p < 0.05) from HT0 to HT8, whereas the resistant variety (Y) showed an inconsistent trend from YT0 to YT8. The pH was decreased (p < 0.05) from HT0 to HT8 and YT0 to YT8. Further, the disease incidence rate and disease index of the H variety also increased (p < 0.05) from HT0 to HT8. Alpha diversity analysis revealed that susceptible variety had higher fungal diversity from HT0 to HT8, while resistant variety exhibited lower diversity from YT0 to YT8. Ascomycota and Mortierellomycota were the dominant phyla in H and Y. Ascomycota abundance was increased (p < 0.05), whereas Mortierellomycota was decreased (p < 0.05) for continuous cropping years in H and Y. Penicillium, Fusarium, and Chrysosporium were the top three abundant genera in both varieties. The relative abundance of Penicillium spp. was increased (p < 0.05) in Y, whereas decreased (p < 0.05) in H variety. Specifically, Chrysosporium spp. was increased (p < 0.05) whereas Fusarium spp. was decreased (p < 0.05) in YT2. Redundancy analysis (RDA) revealed that fungal communities in H and Y rhizospheres were influenced by pH and carbon content, respectively. The top three highly enriched (p < 0.05) pathways in both varieties were fatty acid elongation, fatty acid β-oxidation I, and glyoxylate cycle. Our study concluded that resistant variety exhibited lower fungal diversity and functionally enriched metabolic pathways than susceptible variety that might be the result of molecular breeding practices, however, the relative abundance of Penicillium spp. were increased in resistant variety under long-term monoculture cropping.
Discovery of novel carbohydrate degrading enzymes from soda lakes through functional metagenomics
Authors: Jeilu Oliyad , Simachew Addis , Alexandersson Erik , Johansson Eva , Gessesse Amare
Published: December 2022

Extremophiles provide a one-of-a-kind source of enzymes with properties that allow them to endure the rigorous industrial conversion of lignocellulose biomass into fermentable sugars. However, the fact that most of these organisms fail to grow under typical culture conditions limits the accessibility to these enzymes. In this study, we employed a functional metagenomics approach to identify carbohydrate-degrading enzymes from Ethiopian soda lakes, which are extreme environments harboring a high microbial diversity. Out of 21,000 clones screened for the five carbohydrate hydrolyzing enzymes, 408 clones were found positive. Cellulase and amylase, gave high hit ratio of 1:75 and 1:280, respectively. A total of 378 genes involved in the degradation of complex carbohydrates were identified by combining high-throughput sequencing of 22 selected clones and bioinformatics analysis using a customized workflow. Around 41% of the annotated genes belonged to the Glycoside Hydrolases (GH). Multiple GHs were identified, indicating the potential to discover novel CAZymes useful for the enzymatic degradation of lignocellulose biomass from the Ethiopian soda Lakes. More than 73% of the annotated GH genes were linked to bacterial origins, with Halomonas as the most likely source. Biochemical characterization of the three enzymes from the selected clones (amylase, cellulase, and pectinase) showed that they are active in elevated temperatures, high pH, and high salt concentrations. These properties strongly indicate that the evaluated enzymes have the potential to be used for applications in various industrial processes, particularly in biorefinery for lignocellulose biomass conversion.
Revisiting the immune landscape post spinal cord injury: More than black and white
Authors: Zhen-Gang Liu , Fan Yang , Jingwei Shi , Pengyu Chang , Shengman Yu , Bo-Yin Zhang
Published: December 2022

Spinal cord injury (SCI) induced catastrophic neurological disability is currently incurable, especially in elderly patients. Due to the limited axon regeneration capacity and hostile microenvironment in the lesion site, essential neural network reconstruction remains challenging. Owing to the blood–spinal cord barrier (BSCB) created immune cells and cytokines isolation, the immune elements were incorrectly recognized as innocent bystanders during the SCI pathological process traditionally. Emerging evidence demonstrated that the central nervous system (CNS) is an “immunological quiescent” rather than “immune privileged” area, and the CNS-associated immune response played mixed roles which dedicate beneficial and detrimental contributions throughout the SCI process. Consequently, coordinating double-edged immunomodulation is vital to promote tissue repair and neurological recovery post-SCI. The comprehensive exploration and understanding of the immune landscape post-SCI are essential in establishing new avenues for further basic and clinical studies. In this context, this review summarizes the recent significant breakthroughs in key aspects of SCI-related immunomodulation, including innate and adaptive immune response, immune organ changes, and holistic immune status modification. Moreover, the currently existing immune-oriented therapies for SCI will be outlined.
Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants
Authors: Lyakhov Pavel Alekseevich , Dolgalev Alexander Alexandrovich , Lyakhova Ulyana Alekseevna , Muraev Alexandr Alexandrovich , Zolotayev Kirill Evgenievich , Semerikov Dmitry Yurievich
Published: December 2022

Implants are now the standard method of replacing missing or damaged teeth. Despite the improving technologies for the manufacture of implants and the introduction of new protocols for diagnosing, planning, and performing implant placement operations, the percentage of complications in the early postoperative period remains quite high. In this regard, there is a need to develop new methods for preliminary assessment of the patient’s condition to predict the success of single implant survival. The intensive development of artificial intelligence technologies and the increase in the amount of digital information that is available for analysis make it relevant to develop systems based on neural networks for auxiliary diagnostics and forecasting. Systems based on artificial intelligence in the field of dental implantology can become one of the methods for forming a second opinion based on mathematical decision making and forecasting. The actual clinical evaluation of a particular case and further treatment are carried out by the dentist, and AI-based systems can become an integral part of additional diagnostics. The article proposes an artificial intelligence system for analyzing various patient statistics to predict the success of single implant survival. As the topology of the neural network, the most optimal linear neural network architectures were developed. The one-hot encoding method was used as a preprocessing method for statistical data. The novelty of the proposed system lies in the developed optimal neural network architecture designed to recognize the collected and digitized database of various patient factors based on the description of the case histories. The accuracy of recognition of statistical factors of patients for predicting the success of single implants in the proposed system was 94.48%. The proposed neural network system makes it possible to achieve higher recognition accuracy than similar neural network prediction systems due to the analysis of a large number of statistical factors of patients. The use of the proposed system based on artificial intelligence will allow the implantologist to pay attention to the insignificant factors affecting the quality of the installation and the further survival of the implant, and reduce the percentage of complications at all stages of treatment. However, the developed system is not a medical device and cannot independently diagnose patients. At this point, the neural network system for analyzing the statistical factors of patients can predict a positive or negative outcome of a single dental implant operation and cannot be used as a full-fledged tool for supporting medical decision-making.
Study on the diversity of mental states and neuroplasticity of the brain during human-machine interaction
Authors: Zhang Teng , Zhang Xiaodong , Zhu Wenjing , Lu Zhufeng , Wang Yu , Zhang Yingjie
Published: December 2022

Introduction

With the increasing demand for human-machine collaboration systems, more and more attention has been paid to the influence of human factors on the performance and security of the entire system. Especially in high-risk, high-precision, and difficult special tasks (such as space station maintenance tasks, anti-terrorist EOD tasks, surgical robot teleoperation tasks, etc.), there are higher requirements for the operator’s perception and cognitive level. However, as the human brain is a complex and open giant system, the perception ability and cognitive level of the human are dynamically variable, so that it will seriously affect the performance and security of the whole system.

Methods

The method proposed in this paper innovatively explained this phenomenon from two dimensions of brain space and time and attributed the dynamic changes of perception, cognitive level, and operational skills to the mental state diversity and the brain neuroplasticity. In terms of the mental state diversity, the mental states evoked paradigm and the functional brain network analysis method during work were proposed. In terms of neuroplasticity, the cognitive training intervention paradigm and the functional brain network analysis method were proposed. Twenty-six subjects participated in the mental state evoked experiment and the cognitive training intervention experiment.

Results

The results showed that (1) the mental state of the subjects during work had the characteristics of dynamic change, and due to the influence of stimulus conditions and task patterns, the mental state showed diversity. There were significant differences between functional brain networks in different mental states, the information processing efficiency and the mechanism of brain area response had changed significantly. (2) The small-world attributes of the functional brain network of the subjects before and after the cognitive training experiment were significantly different. The brain had adjusted the distribution of information flow and resources, reducing costs and increasing efficiency as a whole. It was demonstrated that the global topology of the cortical connectivity network was reconfigured and neuroplasticity was altered through cognitive training intervention.

Discussion

In summary, this paper revealed that mental state and neuroplasticity could change the information processing efficiency and the response mechanism of brain area, thus causing the change of perception, cognitive level and operational skills, which provided a theoretical basis for studying the relationship between neural information processing and behavior.
JNK inhibitor IX restrains pancreatic cancer through p53 and p21
Authors: Shi Jingwei , Yang Xing , Kang Qi , Lu Jian , Denzinger Maximilian , Kornmann Marko , Traub Benno
Published: December 2022

Novel treatment options for pancreatic cancer are desperately needed. De-regulated kinases can be regularly detected in pancreatic cancer. Multiple pathway inhibitors were developed to exploit these features, among them selective inhibitors of the c-Jun N-terminal kinase isoforms 1 and 2 (JNK1 and 2). We evaluated the effectiveness of four different JNK inhibitors on pancreatic cancer cell lines. Cell mobility and migration were evaluated in scratch assay and Boyden chamber assay. Mechanism of cell death was analyzed via apoptosis assays in FACS and immunoblotting as well as cell cycle analysis via FACS, and qPCR. JNK2 knockout cells were generated using siRNA transfection. Among the inhibitors, JNK inhibitor IX (JNK-in-IX), designed as specific inhibitor against JNK2 was proven highly effective in inhibiting cell growth, mobility and migration. We were able to show that JNK-in-IX caused DNA damage resulting in G2 arrest mediated through p53 and p21. Interestingly, JNK-in-IX acted independently of its primary target JNK2. In summary, JNK-in-IX was shown highly effective in pancreatic cancer. This study underlines the need for modeling systems in testing therapeutic options as JNK2 was previously not indicated as a potential target.
A differential process mining analysis of COVID-19 management for cancer patients
Authors: Cuendet Michel A. , Gatta Roberto , Wicky Alexandre , Gerard Camille L. , Dalla-Vale Margaux , Tavazzi Erica , Michielin Grégoire , Delyon Julie , Ferahta Nabila , Cesbron Julien , Lofek Sébastien , Huber Alexandre , Jankovic Jeremy , Demicheli Rita , Bouchaab Hasna , Digklia Antonia , Obeid Michel , Peters Solange , Eicher Manuela , Pradervand Sylvain , Michielin Olivier
Published: December 2022

During the acute phase of the COVID-19 pandemic, hospitals faced a challenge to manage patients, especially those with other comorbidities and medical needs, such as cancer patients. Here, we use Process Mining to analyze real-world therapeutic pathways in a cohort of 1182 cancer patients of the Lausanne University Hospital following COVID-19 infection. The algorithm builds trees representing sequences of coarse-grained events such as Home, Hospitalization, Intensive Care and Death. The same trees can also show probability of death or time-to-event statistics in each node. We introduce a new tool, called Differential Process Mining, which enables comparison of two patient strata in each node of the tree, in terms of hits and death rate, together with a statistical significance test. We thus compare management of COVID-19 patients with an active cancer in the first vs. second COVID-19 waves to quantify hospital adaptation to the pandemic. We also compare patients having undergone systemic therapy within 1 year to the rest of the cohort to understand the impact of an active cancer and/or its treatment on COVID-19 outcome. This study demonstrates the value of Process Mining to analyze complex event-based real-world data and generate hypotheses on hospital resource management or on clinical patient care.
HRK inhibits colorectal cancer cells proliferation by suppressing the PI3K/AKT/mTOR pathway
Authors: Wang Haowei , Chen Yujia , Yuan Qinzi , Chen Lixia , Dai Peiling , Li Xuenong
Published: December 2022

Background

As one of the most common malignant tumor, colorectal cancer (CRC) continues to have a high incidence and mortality rate. HRK belongs to the BCL-2 protein family, which has been shown to have antitumor effects in prostate cancer. However, its role in colorectal cancer is not yet known.

Methods

In this study, we verified the expression levels of HRK in colorectal cancer tissues by public database search as well as immunohistochemistry. Next, we analyzed HRK expression levels in CRC tissues,adjacent non-cancerous tissues, cell lines and normal intestinal epithelial cells by qPCR and Western blotting. CCK-8 proliferation assays, transwell assays, wound healing assays, colony assays and flow cytometry were performed to clarified the effect of HRK on CRC cells. Western blotting and rescue experiments were used to determine the role of HRK in regulating PI3K/AKT/mTOR signaling pathway.

Results

HRK expression was lower in CRC tissues and cell lines. Gain and loss of function experiments showed that HRK decreased proliferation, invasion and migration of CRC cells. Low expression of HRK inhibited CRC cell apoptosis as well as activated the PI3K/AKT/mTOR signaling pathway. In addition, rapamycin inhibits the activation of PI3K/AKT/mTOR signaling pathway and reverses HRK-induced alterations in cell biological functions.

Conclusion

Our study demonstrates that HRK is lowly expressed in colorectal cancer tissues. And for the first time, HRK was shown to promote apoptosis and inhibit proliferation of colorectal cancer cells by inhibiting PI3K/AKT/mTOR signaling pathway. HRK represents a potential target for the treatment of CRC.
Expression, tumor immune infiltration, and prognostic impact of HMGs in gastric cancer
Authors: Wu Zhiheng , Huang Yang , Yuan Weiwei , Wu Xiong , Shi Hui , Lu Ming , Xu Aman
Published: December 2022

Background

In the past decade, considerable research efforts on gastric cancer (GC) have been expended, however, little advancement has been made owing to the lack of effective biomarkers and treatment options. Herein, we aimed to examine the levels of expression, mutations, and clinical relevance of HMGs in GC to provide sufficient scientific evidence for clinical decision-making and risk management.

Methods

GC samples were obtained from The Cancer Genome Atlas (TCGA). University of California Santa Cruz (UCSC) XENA, Human Protein Atlas (HPA), Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier Plotter, cBioPortal, GeneMANIA, STRING, LinkedOmics, and DAVID databases were employed. The “ggplot2” package in the R software (×64 3.6.3) was used to thoroughly analyze the effects of HMGs. qRT-PCR was performed to assess HMG levels in GC cell lines.

Results

A total of 375 GC tissues and 32 paraneoplastic tissues were analyzed. The levels of HMGA1, HMGA2, HMGB1, HMGB2, HMGB3, HMGN1, HMGN2, and HMGN4 expression were increased in GC tissues relative to normal gastric tissues. HMGA1, HMGA2, HMGB1, HMGB2, and HMGB3 were highly expressed in GC cell lines. The OS was significantly different in the group showing low expressions of HMGA1, HMGA2, HMGB1, HMGB2, HMGB3, HMGN2, HMGN3, and HMGN5. There was a significant difference in RFS between the groups with low HMGA2, HMGB3, and high HMGN2 expression. The levels of HMGA2, HMGB3, and HMGN1 had a higher accuracy for prediction to distinguish GC from normal tissues (AUC value > 0.9). HMGs were tightly associated with immune infiltration and tumor immune escape and antitumor immunity most likely participates in HMG-mediated oncogenesis in GC. GO and KEGG enrichment analyses showed that HMGs played a vital role in the cell cycle pathway.

Conclusions

Our results strongly suggest a vital role of HMGs in GC. HMGA2 and HMGB3 could be potential markers for prognostic prediction and treatment targets for GC by interrupting the cell cycle pathway. Our findings might provide renewed perspectives for the selection of prognostic biomarkers among HMGs in GC and may contribute to the determination of the optimal strategy for the treatment of these patients.
A novel risk classification system based on the eighth edition of TNM frameworks for esophageal adenocarcinoma patients: A deep learning approach
Authors: Shen Qiang , Chen Hongyu
Published: December 2022

Objective

To develop and validate a deep learning predictive model with better performance in survival estimation of esophageal adenocarcinoma (EAC).

Method

Cases diagnosed between January 2010 and December 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. A deep learning survival neural network was developed and validated based on 17 variables, including demographic information, clinicopathological characteristics, and treatment details. Based on the total risk score derived from this algorithm, a novel risk classification system was constructed and compared with the 8th edition of the tumor, node, and metastasis (TNM) staging system.

Results

Of 7,764 EAC patients eligible for the study, 6,818 (87.8%) were men and the median (interquartile range, IQR) age was 65 (58–72) years. The deep learning model generated significantly superior predictions to the 8th edition staging system on the test data set (C-index: 0.773 [95% CI, 0.757–0.789] vs. 0.683 [95% CI, 0.667–0.699]; P < 0.001). Calibration curves revealed that the deep learning model was well calibrated for 1- and 3-year OS, most points almost directly distributing on the 45° line. Decision curve analyses (DCAs) showed that the novel risk classification system exhibited a more significant positive net benefit than the TNM staging system. A user-friendly and precise web-based calculator with a portably executable file was implemented to visualize the deep learning predictive model.

Conclusion

A deep learning predictive model was developed and validated, which possesses more excellent calibration and discrimination abilities in survival prediction of EAC. The novel risk classification system based on the deep learning algorithm may serve as a useful tool in clinical decision making given its easy-to-use and better clinical applicability.
Pre-transplant gene profiling characterization by next-generation DNA sequencing might predict relapse occurrence after hematopoietic stem cell transplantation in patients affected by AML
Authors: Metafuni Elisabetta , Amato Viviana , Giammarco Sabrina , Bellesi Silvia , Rossi Monica , Minnella Gessica , Frioni Filippo , Limongiello Maria Assunta , Pagano Livio , Bacigalupo Andrea , Sica Simona , Chiusolo Patrizia
Published: December 2022

Background

In the last decade, many steps forward have been made in acute myeloid leukemia prognostic stratification, adding next-generation sequencing techniques to the conventional molecular assays. This resulted in the revision of the current risk classification and the introduction of new target therapies.

Aims and methods

We wanted to evaluate the prognostic impact of acute myeloid leukemia (AML) mutational pattern on relapse occurrence and survival after allogeneic stem cell transplantation. A specific next-generation sequencing (NGS) panel containing 26 genes was designed for the study. Ninety-six patients studied with NGS at diagnosis were included and retrospectively studied for post-transplant outcomes.

Results

Only eight patients did not show any mutations. Multivariate Cox regression revealed FLT3 (HR, 3.36; p=0.02), NRAS (HR, 4.78; p=0.01), TP53 (HR, 4.34; p=0.03), and WT1 (HR 5.97; p=0.005) mutations as predictive variables for relapse occurrence after transplantation. Other independent variables for relapse recurrence were donor age (HR, 0.97; p=0.04), the presence of an adverse cytogenetic risk at diagnosis (HR, 3.03; p=0.04), and the obtainment of complete remission of the disease before transplantation (HR, 0.23; p=0.001). Overall survival appeared to be affected only by grade 2–4 acute GvHD occurrence (HR, 2.29; p=0.05) and relapse occurrence (HR, 4.33; p=0.0001) in multivariate analysis.

Conclusions

The small number of patients and the retrospective design of the study might affect the resonance of our data. Although results on TP53, FLT3, and WT1 were comparable to previous reports, the interesting data on NRAS deserve attention.
Clinical implication and management of rectal cancer with clinically suspicious lateral pelvic lymph node metastasis: A radiation oncologist’s perspective
Authors: Yoo Gyu Sang , Park Hee Chul , Yu Jeong Il
Published: December 2022

Rectal cancer is the eighth most common malignancy worldwide. With the introduction of total mesorectal excision (TME) and neoadjuvant chemoradiation (NCRT), intrapelvic local control has been remarkably improved. However, lateral pelvic recurrence remains problematic, especially in patients with clinically suspicious lateral pelvic lymph node (LPLN). LPLN dissection has been applied for the management of LPLN metastasis, mainly in Japan and other Eastern countries, while the role of NCRT is more emphasized and LPLN dissection is performed in very limited cases in Western countries. However, the optimal management strategy for patients with rectal cancer with suspicious LPLN metastasis has not been determined. Herein, we review the latest studies on the optimal management of LPLN metastasis to suggest the most appropriate treatment policies according to current evidence and discuss future research directions.
Quantification of the growth suppression of HER2+ breast cancer colonies under the effect of trastuzumab and PD-1/PD-L1 inhibitor
Authors: Padmanabhan Regina , Kheraldine Hadeel , Gupta Ishita , Meskin Nader , Hamad Anas , Vranic Semir , Al Moustafa Ala-Eddin
Published: December 2022

Introduction

Immune checkpoint blockade (ICB)-based therapy is revolutionizing cancer treatment by fostering successful immune surveillance and effector cell responses against various types of cancers. However, patients with HER2+ cancers are yet to benefit from this therapeutic strategy. Precisely, several questions regarding the right combination of drugs, drug modality, and effective dose recommendations pertaining to the use of ICB-based therapy for HER2+ patients remain unanswered.

Methods

In this study, we use a mathematical modeling-based approach to quantify the growth inhibition of HER2+ breast cancer (BC) cell colonies (ZR75) when treated with anti-HER2; trastuzumab (TZ) and anti-PD-1/PD-L1 (BMS-202) agents.

Results and discussion

Our data show that a combination therapy of TZ and BMS-202 can significantly reduce the viability of ZR75 cells and trigger several morphological changes. The combination decreased the cell’s invasiveness along with altering several key pathways, such as Akt/mTor and ErbB2 compared to monotherapy. In addition, BMS-202 causes dose-dependent growth inhibition of HER2+ BC cell colonies alone, while this effect is significantly improved when used in combination with TZ. Based on the in-vitro monoculture experiments conducted, we argue that BMS-202 can cause tumor growth suppression not only by mediating immune response but also by interfering with the growth signaling pathways of HER2+BC. Nevertheless, further studies are imperative to substantiate this argument and to uncover the potential crosstalk between PD-1/PD-L1 inhibitors and HER2 growth signaling pathways in breast cancer.
Outpatient monitoring of patients with multisystem inflammatory syndrome (MIS-C): A mini review
Authors: Jose Jerin Tresa , Selamet Tierney Elif Seda
Published: December 2022

Introduction

As we learn more about the novel multisystem inflammatory syndrome in children (MIS-C) associated with COVID-19 infection, the protocols for long-term follow-up have evolved and only some of these protocols have been published. Here, we review the current literature on follow-up guidelines in MIS-C patients.

Methods

We conducted a PUBMED search of all articles published on “MIS-C” and the term “follow-up” between 2020 and 2022. Inclusion criteria were that (1) the study was an observational study or case series, and (2) the study population included pediatric population who met the diagnostic criteria for MIS-C.

Results

There were 206 publications on MIS-C and follow-up in the last 2 years with 11 studies that fit the inclusion criteria. These papers were representing 11 different centers and encompassed a total of 343 participants. Seven of the 11 studies had participants follow-up with their cardiologist within 1 month of discharge. Between 12% and 62% of patients within each study had depressed left ventricular ejection fraction (LVEF) at admission. At the initial follow-up visit, five studies showed a normal LVEF in all patients while the other seven studies showed 2%–13% patients continuing to have depressed LVEF. In eight of the 11 studies, 9%–52% of patients had coronary artery dilation at admission. At their initial follow-up visit, 3%–28% of patients continued to have coronary artery dilation.

Conclusion

There is some institutional variation in the outpatient follow-up protocols in patients diagnosed with MIS-C. A standardized follow-up guidelines might be helpful to monitor long-term prognosis of these patients.
Anticancer activity of glycoalkaloids from Solanum plants: A review
Authors: Winkiel Magdalena Joanna , Chowański Szymon , Słocińska Małgorzata
Published: December 2022

Cancer is still one of the main causes of death worldwide. For this reason, new compounds that have chemotherapeutic potential have been identified. One such group of substances is Solanaceae glycoalkaloids (GAs). They are natural compounds produced by plants widely used in traditional medicine for healing many disorders. Among others, GAs exhibit significant antitumor properties, for example, a strong inhibitory effect on cancer cell growth. This activity can result in the induction of tumor cell apoptosis, which can occur via different molecular pathways. The molecular mechanisms of the action of GAs are the subject of intensive research, as improved understanding could lead to the development of new cancer therapies. The genetic basis for the formation of neoplasms are mutations in protooncogenes, suppressors, and apoptosis-controlling and repair genes; therefore, substances with antineoplastic properties may affect the levels of their expression or the levels of their expression products. Therapeutic compounds can be applied separately or in combination with other drugs to increase the efficiency of cancer therapy; they can act on the cell through various mechanisms at different stages of carcinogenesis, inducing the process of apoptosis, blocking cell proliferation and migration, and inhibiting angiogenesis. This review summarizes the newest studies on the anticancer properties of solanine (SN), chaconine (CH), solasonine (SS), solamargine (SM), tomatine (TT) and their extracts from Solanum plants.
Single-shot attenuation coefficient estimation for ultrasound contrast agents
Authors: Birdi Jasleen , Heymans Sophie V. , Collado-Lara Gonzalo , Van Den Abeele Koen , D’hooge Jan , Bertrand Alexander
Published: December 2022

Ultrasound contrast agents (UCAs) have broadened the scope of ultrasound imaging and therapeutic applications. One of the parameters of interest when measuring the response of UCAs to ultrasound is their frequency-dependent attenuation coefficient. The estimation of this parameter is relevant for sensing and therapeutic applications, as well as for obtaining the viscoelastic properties of the UCA’s shell. The current practice to obtain this coefficient relies on experimental measurements made both in the presence and absence of UCAs in a target medium. Not only is the microbubble-free reference measurement time-consuming, but it may also not always be feasible for in vivo applications due to lack of an appropriate reflector. To overcome these challenges, we present here a novel approach which estimates the UCA’s attenuation spectra directly from pulse-echo measurements made in the underlying UCA medium, without any reference measurement. Furthermore, despite the non-linear frequency dependency of the UCA’s attenuation profile, our approach can still benefit from a fast linear least-squares based estimation scheme, providing attenuation estimates in a single-shot, which is desirable for implementation in real-time systems. We provide an investigative study, testing the estimator’s performance on various simulated realistic attenuation profiles obtained by varying the shell parameters and the UCA’s size distribution. In all cases, the estimated attenuation profiles were in good agreement with the true ones, with a relative error < 10%. Evaluation on experimental in vitro data shows a relative error < 15%, which further highlights the potential of our approach for fast and accurate UCA’s attenuation estimation.
The interface between international trade and investment agreements and food environment policymaking: A conceptual framework
Authors: Garton Kelly , Swinburn Boyd , Thow Anne Marie
Published: December 2022

Addressing the global challenge of malnutrition in all its forms will require policy measures to improve food environments, yet progress has been patchy and often slow, particularly for regulatory measures. International trade and investment agreements (TIAs) may limit governments' “policy space” for public health regulation. Constraints have been particularly apparent for public health measures targeting unhealthy commodities, including ultra-processed foods. Challenges and disputes regarding food environment regulation under TIAs (even if successfully defended) can entail significant drain of human and financial resources, and political capital. Lack of awareness or understanding of the implication of TIAs on policy space for regulation can contribute to regulatory chill and policy inertia. Governments lacking capacity to interpret their “legally available” policy space may want to err on the side of caution when there is perceived risk of a formal dispute—even if such threats are unfounded. This paper draws on analysis of literature, trade and investment dispute documentation, and data from inter-disciplinary expert interviews (n= 22) to present a new conceptual framework for the potential impacts of TIAs on policy space for regulating food environments. The analysis that underpins the framework focusses on the key policy domains of fiscal policies, front-of-pack nutrition labeling, restrictions on marketing to children, nutrient limits, and product bans. Analysis indicates that regulatory context and stakeholder influence, policy design, and mechanisms associated with TIA rules and provisions intersect in ways contributing to policy space outcomes. This new framework can provide a basis for rapidly assessing policy coherence between TIAs and food environment regulations in these domains. It can also be used to identify areas where further legal analysis would strengthen the development and defense of regulatory proposals. The framework may be applied to nutrition regulation more broadly, given the common themes that emerged across the different domains due to common interests of stakeholders, notably the food industry. It thus provides a basis for analyzing the political economy of regulation to address the commercial determinants of health in relation to unhealthy food and beverages.
Perception of Japanese singleton and geminate contrasts: A case of Chinese learners with different dialectal backgrounds
Authors: Ren Honghao
Published: December 2022

It is widely accepted that the Japanese language is mora-timed, and the geminate obstruent, one of the three special morae in the Japanese language, forms one independent mora. Many studies have shown that perceiving Japanese geminates accurately is especially problematic for learners of Japanese. This study examines the perception of Japanese singleton and geminate contrasts by Chinese learners of Japanese (CLJ) from different dialectal backgrounds and contrasts their perception with Japanese native speakers (JNS). This study conducted two experiments. First, non-synthesized stimuli were used to test each group of participants’ perceptual sensitivity of Japanese singletons and geminates. Second, the categorical perception was accessed by adopting stimuli in which the ratio of constriction duration to entire word duration (CD/EWD) was synthesized from 15 to 60%. Results show that, although learners of Japanese had significantly lower perception levels compared to JNS regardless of their group differences (e.g., L1 background, Japanese language proficiency, etc.), they still experienced some positive transfers of L1 to achieve better perception performance. The results also suggest that CD/EWD can be considered a reliable local cue for both JNS and CLJ in categorizing Japanese singletons and geminates. In addition, the results demonstrate that variables such as medial consonant type and the learner’s Japanese proficiency affect the perception of Japanese singleton and geminate contrasts.
Interventions simultaneously promoting social participation and physical activity in community living older adults: A systematic review
Authors: Tcymbal Antonina , Abu-Omar Karim , Hartung Verena , Bußkamp Annalena , Comito Chiara , Rossmann Christin , Meinzinger Diana , Reimers Anne Kerstin
Published: December 2022

Background

In recent years, there has been a global trend toward an increase in life expectancy and the proportion of elderly people among the population. In this regard, it becomes important to promote active and healthy aging. Physical inactivity and social isolation are both risk factors of many chronic illnesses and highly prevalent in older adults. This challenges communities to develop interventions that reduce these risk factors among elderly populations. The main aims of this study were to summarize community-based interventions that aim to simultaneously promote social participation and physical activity in older adults and to examine their effects.

Methods

We performed a systematic review based on the PRISMA standards. Literature searches were conducted in six scientific databases in July 2021. Articles were included if they had an interventional design, focused on older adults living in the community and measured social participation and physical activity as an outcome. The data were summarized narratively due to the heterogeneity of studies and the variety of outcome measures.

Results

Overall, 46 articles published in English were included. The studies were grouped in (1) interventions with main focus on physical activity promotion; (2) social activities that included a physical activity component; (3) health behavior interventions/ health education interventions; (4) multicomponent interventions; (5) environmental interventions. The majority of the reviewed studies reported positive effects of interventions on physical activity and/or social participation. No study reported negative effects. Analysis of quantitative studies showed that multicomponent interventions have great positive effects on both outcomes. In qualitative studies positive effects were found regardless of intervention type.

Conclusion

This review summarizes the evidence about the effects of community-based interventions that aim to promote social participation and physical activity in older adults. Multicomponent interventions seem to be most suitable for simultaneous promotion of physical activity and social participation. However, high variability in measurement methods used to assess both social participation and physical activity in the included studies made it difficult to compare studies and to indicate the most effective.

Systematic review registration

www.crd.york.ac.uk, identifier: PROSPERO [CRD42021268270].
Embodied airflow sensing for improved in-gust flight of flapping wing MAVs
Authors: Wang Chenyao , Wang Sunyi , De Croon Guido , Hamaza Salua
Published: December 2022

Flapping wing micro aerial vehicles (FWMAVs) are known for their flight agility and maneuverability. These bio-inspired and lightweight flying robots still present limitations in their ability to fly in direct wind and gusts, as their stability is severely compromised in contrast with their biological counterparts. To this end, this work aims at making in-gust flight of flapping wing drones possible using an embodied airflow sensing approach combined with an adaptive control framework at the velocity and position control loops. At first, an extensive experimental campaign is conducted on a real FWMAV to generate a reliable and accurate model of the in-gust flight dynamics, which informs the design of the adaptive position and velocity controllers. With an extended experimental validation, this embodied airflow-sensing approach integrated with the adaptive controller reduces the root-mean-square errors along the wind direction by 25.15% when the drone is subject to frontal wind gusts of alternating speeds up to 2.4 m/s, compared to the case with a standard cascaded PID controller. The proposed sensing and control framework improve flight performance reliably and serve as the basis of future progress in the field of in-gust flight of lightweight FWMAVs.
GeospaceLAB: Python package for managing and visualizing data in space physics
Authors: Cai Lei , Aikio Anita , Kullen Anita , Deng Yue , Zhang Yongliang , Zhang Shun-Rong , Virtanen Ilkka , Vanhamäki Heikki
Published: December 2022

In the space physics community, processing and combining observational and modeling data from various sources is a demanding task because they often have different formats and use different coordinate systems. The Python package GeospaceLAB has been developed to provide a unified, standardized framework to process data. The package is composed of six core modules, including DataHub as the data manager, Visualization for generating publication quality figures, Express for higher-level interfaces of DataHub and Visualization, SpaceCoordinateSystem for coordinate system transformations, Toolbox for various utilities, and Configuration for preferences. The core modules form a standardized framework for downloading, storing, post-processing and visualizing data in space physics. The object-oriented design makes the core modules of GeospaceLAB easy to modify and extend. So far, GeospaceLAB can process more than twenty kinds of data products from nine databases, and the number will increase in the future. The data sources include, e.g., measurements by EISCAT incoherent scatter radars, DMSP, SWARM, and Grace satellites, OMNI solar wind data, and GITM simulations. In addition, the package provides an interface for the users to add their own data products. Hence, researchers can easily collect, combine, and view multiple kinds of data for their work using GeospaceLAB. Combining data from different sources will lead to a better understanding of the physics of the studied phenomena and may lead to new discoveries. GeospaceLAB is an open source software, which is hosted on GitHub. We welcome everyone in the community to contribute to its future development.
Supporting responsible machine learning in heliophysics
Authors: Narock Ayris , Bard Christopher , Thompson Barbara J. , Halford Alexa J. , McGranaghan Ryan M. , da Silva Daniel , Kosar Burcu , Shumko Mykhaylo
Published: December 2022

Over the last decade, Heliophysics researchers have increasingly adopted a variety of machine learning methods such as artificial neural networks, decision trees, and clustering algorithms into their workflow. Adoption of these advanced data science methods had quickly outpaced institutional response, but many professional organizations such as the European Commission, the National Aeronautics and Space Administration (NASA), and the American Geophysical Union have now issued (or will soon issue) standards for artificial intelligence and machine learning that will impact scientific research. These standards add further (necessary) burdens on the individual researcher who must now prepare the public release of data and code in addition to traditional paper writing. Support for these is not reflected in the current state of institutional support, community practices, or governance systems. We examine here some of these principles and how our institutions and community can promote their successful adoption within the Heliophysics discipline.