bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lactate promotes the growth of patient-derived organoids from hepatopancreatobiliary cancers via ENO1/HIF1α pathway and does not affect their drug sensitivities
Authors: Wang Zhiwei , Yu Yuanquan , Wu Peiyao , Ye Qinghuang , Guo Yinghao , Zhang Xiaoxiao , Xi Longfu , Li Qi , Jin Yun , Zhou Donger , Luo Yan , Peng Shuyou , Li Jiangtao 
Published: April 2022

AbstractThe long culture duration of patient-derived organoids (PDOs) have severely limited their clinical applications. The aim of this study was to determine the effect of lactate supplementation on the growth, genetic profiles and drug sensitivities of PDOs from hepatopancreatobiliary tumors. LM3, Huh7, Panc02, and RBE cell lines were cultured as organoids in the presence or absence of lactate, and total protein was extracted to measure the expression of α-enolase (ENO1), hypoxia-inducible factor-1α (HIF1α), AKT, and PI3 kinase (PI3K). Thirteen hepatopancreatobiliary tumor specimens were collected during surgical resection and cultured as PDOs with or without l-lactate. Hematoxylin and eosin (H&E) staining and immunohistochemical staining were performed on the original tissues and PDOs to compare their pathological structures, and their genetic profiles were analyzed by whole-exome sequencing (WES). The sensitivity of the PDOs to gemcitabine, 5-fluorouracil, cisplatin, paclitaxel, ivosidenib, infigratinib, and lenvatinib were evaluated in terms of cell viability. Peripheral blood mononuclear cells (PBMCs) were isolated and co-cultured with PDOs to test the sensitivity of PDOs to tislelizumab. The addition of 20 mM lactate significantly promoted the growth of LM3 and Huh 7 organoids by 217% and 36%, respectively, compared to the control group, and the inhibition of lactate transporter decreased their growth. The HIF1α/ENO1/AKT/PI3K pathway was also activated by lactate. The inhibition of enolase also partly decreased the growth of organoids treated with lactate. Furthermore, 20 mM lactate increased the viability of 9 PDOs from 135% to 317% without affecting their pathological features. The genetic similarity, in terms of single nucleotide variations, insertions, and deletions, between original tissues and lactate-treated PDOs ranged from 83.2% to 94.1%, and that between the untreated and lactate-treated PDOs was at least 93.2%. Furthermore, the addition of lactate did not significantly change the dose–response curves of the PDOs to chemotherapeutic drugs, targeted drugs, and immune checkpoint inhibitor, especially for the drugs to which the cells were sensitive. Thus, lactate can be added to the culture medium of PDOs to promote their growth without altering their genetic profiles and drug sensitivities.
Discovering equations that govern experimental materials stability under environmental stress using scientific machine learning
Authors: Naik Richa Ramesh , Tiihonen Armi , Thapa Janak , Batali Clio , Liu Zhe , Sun Shijing , Buonassisi Tonio 
Published: April 2022

AbstractWhile machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, extracting fungible knowledge representations from experimental data remains an elusive task. In this manuscript, we use ML to infer the underlying differential equation (DE) from experimental data of degrading organic-inorganic methylammonium lead iodide (MAPI) perovskite thin films under environmental stressors (elevated temperature, humidity, and light). Using a sparse regression algorithm, we find that the underlying DE governing MAPI degradation across a broad temperature range of 35 to 85 °C is described minimally by a second-order polynomial. This DE corresponds to the Verhulst logistic function, which describes reaction kinetics analogous to self-propagating reactions. We examine the robustness of our conclusions to experimental variance and Gaussian noise and describe the experimental limits within which this methodology can be applied. Our study highlights the promise and challenges associated with ML-aided scientific discovery by demonstrating its application in experimental chemical and materials systems.
Perovskite synthesizability using graph neural networks
Authors: Gu Geun Ho , Jang Jidon , Noh Juhwan , Walsh Aron , Jung Yousung 
Published: April 2022

AbstractPerovskite is an important material type in geophysics and for technologically important applications. However, the number of synthetic perovskites remains relatively small. To accelerate the high-throughput discovery of perovskites, we propose a graph neural network model to assess their synthesizability. Our trained model shows a promising 0.957 out-of-sample true positive rate, significantly improving over empirical rule-based methods. Further validation is established by demonstrating that a significant portion of the virtual crystals that are predicted to be synthesizable have already been indeed synthesized in literature, and those with the lowest synthesizability scores have not been reported. While previous empirical strategies are mainly applicable to metal oxides, our model is general and capable of predicting the synthesizability across all classes of perovskites, including chalcogenide, halide, and hydride perovskites, as well as anti-perovskites. We apply the method to identify synthesizable perovskite candidates for two potential applications, the Li-rich ion conductors and metal halide optical materials that can be tested experimentally.
Comprehensive network medicine-based drug repositioning via integration of therapeutic efficacy and side effects
Authors: Paci Paola , Fiscon Giulia , Conte Federica , Wang Rui-Sheng , Handy Diane E. , Farina Lorenzo , Loscalzo Joseph 
Published: April 2022

AbstractDespite advances in modern medicine that led to improvements in cardiovascular outcomes, cardiovascular disease (CVD) remains the leading cause of mortality and morbidity globally. Thus, there is an urgent need for new approaches to improve CVD drug treatments. As the development time and cost of drug discovery to clinical application are excessive, alternate strategies for drug development are warranted. Among these are included computational approaches based on omics data for drug repositioning, which have attracted increasing attention. In this work, we developed an adjusted similarity measure implemented by the algorithm SAveRUNNER to reposition drugs for cardiovascular diseases while, at the same time, considering the side effects of drug candidates. We analyzed nine cardiovascular disorders and two side effects. We formulated both disease disorders and side effects as network modules in the human interactome, and considered those drug candidates that are proximal to disease modules but far from side-effects modules as ideal. Our method provides a list of drug candidates for cardiovascular diseases that are unlikely to produce common, adverse side-effects. This approach incorporating side effects is applicable to other diseases, as well.
Severe acute kidney disease is associated with worse kidney outcome among acute kidney injury patients
Authors: Chen Yu-Wei , Wu Mei-Yi , Mao Cheng-Hsien , Yeh Yu-Ting , Chen Tzu-Ting , Liao Chia-Te , Zheng Cai-Mei , Hsu Yung-Ho , Cherng Yih-Giun , Wu Mai-Szu 
Published: April 2022

AbstractAcute kidney disease (AKD) comprises acute kidney injury (AKI). However, whether the AKD staging system has prognostic values among AKI patients with different baseline estimated glomerular filtration (eGFR) remains a controversial issue. Algorithm-based approach was applied to identify AKI occurrence and to define different AKD stages. Risk ratio for major adverse kidney events (MAKE), including (1) eGFR decline > 35% from baseline, (2) initiation of dialysis, (3) in-hospital mortality of different AKD subgroups were identified by multivariable logistic regression. Among the 4741 AKI patients identified from January 2015 to December 2018, AKD stages 1–3 after AKI was common (53% in the lower baseline eGFR group and 51% in the higher baseline eGFR group). In the logistic regression model adjusted for demographics and comorbidities at 1-year follow-up, AKD stages 1/2/3 (AKD stage 0 as reference group) were associated with higher risks of MAKE (AKD stage: odds ratio, 95% confidence interval [95% CI], AKD 1: 1.85, 1.56–2.19; AKD 2: 3.43, 2.85–4.12; AKD 3: 10.41, 8.68–12.49). Regardless of baseline eGFR, staging criteria for AKD identified AKI patients who were at higher risk of kidney function decline, dialysis and mortality. Post-AKI AKD patients with severer stage need intensified care and timely intervention.
Experimental validation of state equations and dynamic route maps for phase change memristive devices
Authors: Marrone Francesco , Secco Jacopo , Kersting Benedikt , Le Gallo Manuel , Corinto Fernando , Sebastian Abu , Chua Leon O. 
Published: April 2022

AbstractPhase Change Memory (PCM) is an emerging technology exploiting the rapid and reversible phase transition of certain chalcogenides to realize nanoscale memory elements. PCM devices are being explored as non-volatile storage-class memory and as computing elements for in-memory and neuromorphic computing. It is well-known that PCM exhibits several characteristics of a memristive device. In this work, based on the essential physical attributes of PCM devices, we exploit the concept of Dynamic Route Map (DRM) to capture the complex physics underlying these devices to describe them as memristive devices defined by a state—dependent Ohm’s law. The efficacy of the DRM has been proven by comparing numerical results with experimental data obtained on PCM devices.
Smart-parking management algorithms in smart city
Authors: Jemmali Mahdi , Melhim Loai Kayed B. , Alharbi Mafawez T. , Bajahzar Abdullah , Omri Mohamed Nazih 
Published: April 2022

AbstractRecently, various advanced technologies have been employed to build smart cities. Smart cities aim at improving the quality of life through the delivery of better services. One of the current services that are essential for any smart city, is the availability of enough parking spaces to ensure smooth and easy traffic flow. This research proposes a new framework for solving the problem of parking lot allocation, which emphasizes the equitable allocation of people based on the overall count of people in each parking space. The allocation process is performed while considering the available parking lots in each parking space. To accomplish the desired goal, this research will develop a set of seven algorithms to reduce the gap in the number of people between parking spaces. Many experiments carried out on 2430 different cases to cover several aspects such as the execution time and the gap calculations, were used to explore the performance of the developed algorithm. Analyzing the obtained results indicates a good performance behavior of the developed algorithms. Also, it shows that the developed algorithms can solve the studied problem in terms of gap and time calculations. The MR algorithm gained excellent performance results compared to one of the best algorithms in the literature. The MR algorithm has a percentage of 96.1 %, an average gap of 0.02, and a good execution time of 0.007 s.
Biochar from fungiculture waste for adsorption of endocrine disruptors in water
Authors: Vieira Raquel Andrade Leite , Pickler Thaisa Borim , Segato Talita Cristina Mena , Jozala Angela Faustino , Grotto Denise 
Published: April 2022

AbstractThe agricultural residues are ecofriendly alternatives for removing contaminants from water. In this way, a novel biochar from the spent mushroom substrate (SMS) was produced and assessed to remove endocrine disruptor from water in batch and fixed-bed method. SMS were dried, ground, and pyrolyzed. Pyrolysis was carried out in three different conditions at 250 and 450 °C, with a residence time of 1 h, and at 600 °C with a residence time of 20 min. The biochar was firstly tested in a pilot batch with 17α-ethinylestradiol (EE2) and progesterone. The residual concentrations of the endocrine disruptors were determined by HPLC. The biochar obtained at 600 °C showed the best removal efficiency results. Then, adsorption parameters (isotherm and kinetics), fixed bed tests and biochar characterization were carried out. The Langmuir model fits better to progesterone while the Freundlich model fits better to EE2. The Langmuir model isotherm indicated a maximum adsorption capacity of 232.64 mg progesterone/g biochar, and 138.98 mg EE2/g biochar. Images from scanning electrons microscopy showed that the 600 °C biochar presented higher porosity than others. In the fixed bed test the removal capacity was more than 80% for both endocrine disruptors. Thus, the biochar showed a good and viable option for removal of contaminants, such as hormones.
Cross-reactive antibodies elicited to conserved epitopes on SARS-CoV-2 spike protein after infection and vaccination
Authors: Geanes Eric S. , LeMaster Cas , Fraley Elizabeth R. , Khanal Santosh , McLennan Rebecca , Grundberg Elin , Selvarangan Rangaraj , Bradley Todd 
Published: April 2022

AbstractSARS-CoV-2 is a novel betacoronavirus that caused coronavirus disease 2019 and has resulted in millions of deaths worldwide. Novel coronavirus infections in humans have steadily become more common. Understanding antibody responses to SARS-CoV-2, and identifying conserved, cross-reactive epitopes among coronavirus strains could inform the design of vaccines and therapeutics with broad application. Here, we determined that individuals with previous SARS-CoV-2 infection or vaccinated with the Pfizer-BioNTech BNT162b2 vaccine produced antibody responses that cross-reacted with related betacoronaviruses. Moreover, we designed a peptide-conjugate vaccine with a conserved SARS-CoV-2 S2 spike epitope, immunized mice and determined cross-reactive antibody binding to SARS-CoV-2 and other related coronaviruses. This conserved spike epitope also shared sequence homology to proteins in commensal gut microbiota and could prime immune responses in humans. Thus, SARS-CoV-2 conserved epitopes elicit cross-reactive immune responses to both related coronaviruses and host bacteria that could serve as future targets for broad coronavirus therapeutics and vaccines.
Coupling reconstruction of atmospheric hydrological profile and dry-up risk prediction in a typical lake basin in arid area of China
Authors: Wang Jie , Liu Dongwei , Tian Songni , Ma Jiali , Wang Lixin 
Published: April 2022

AbstractArid area is very sensitive to global warming and are extremely vulnerable to climate change. Moreover, the water resources system in the arid area is fragile and will undergo tremendous changes with climate change. Therefore, the interaction of climate and hydrology in arid area has an important impact on the formation of regional microclimate and hydrological changes. Daihai Lake is a typical closed inland lake in arid area of China, and a key area for ecological protection in North China. In this paper, WRF-Hydro model is used to simulate the climate hydrological coupling situation of Daihai Basin from 1980 to 2020, and the coupling results are verified and calibrated by meteorological statistics, runoff calculation and remote sensing analysis. Based on the synopsis of climate and hydrology in the past 40 years, the causes and future trends of the hydrological elements in Daihai Basin are analyzed. Through the analysis, it is found that the interannual variation of precipitation in Daihai Basin is sharp, with 401.75 mm as the average from 1980 to 1994; and drastic fluctuations from 1995 to 2011, with a difference of nearly 400 mm between the interannual maximum and minimum; From 2012 to 2020, the fluctuation is small. Although the interannual variation of evaporation fluctuated, it showed an upward trend with a slope of 8.855 mm/year. The annual average temperature showed an obvious upward trend with a slope of 0.040 °C/year. From 1980 to 2020, the inflow of Daihai Lake shows a downward trend; Since 2013, the runoff into the lake has tended to be flat. Climate change and human activities are the decisive factors leading to the change of water quantity in Daihai, among which human activities play a greater role. Cultivated land irrigation and industrial water use are highly correlated with the lake discharge, and these two factors have a great influence on the lake discharge. If the current agricultural and industrial water consumption does not increase, Daihai still has a lifespan of nearly 120 years. If human activities do not change and any protective measures are not taken in time, under the background of global climate change, the flow of the Daihai Lake into the lake will be reduced to zero in 2025, and the Daihai Lake will completely dry up in 2031–2033. The study of climate hydrological coupling of long time series in Daihai Basin can not only make up for the lack of runoff data, but also provide the basis for water resources management, disaster prevention and mitigation.
Beneficial effects of a polysaccharide-based grinding aid on magnetite flotation: a green approach
Authors: Chipakwe Vitalis , Karlkvist Tommy , Rosenkranz Jan , Chelgani Saeed Chehreh 
Published: April 2022

AbstractGrinding is the most energy-intensive step in mineral beneficiation processes. The use of grinding aids (GAs) could be an innovative solution to reduce the high energy consumption associated with size reduction. Surprisingly, little is known about the effects of GAs on downstream mineral beneficiation processes, such as flotation separation. The use of ecofriendly GAs such as polysaccharide-based materials would help multiply the reduction of environmental issues in mineral processing plants. As a practical approach, this work explored the effects of a novel polysaccharide-based grinding aid (PGA) on magnetite's grinding and its reverse flotation. Batch grinding tests indicated that PGA improved grinding performance by reducing energy consumption, narrowing particle size distribution of products, and increasing their surface area compared to grinding without PGA. Flotation tests on pure samples illustrated that PGA has beneficial effects on magnetite depression (with negligible effect on quartz floatability) through reverse flotation separation. Flotation of the artificial mixture ground sample in the presence of PGA confirmed the benefits, giving a maximum Fe recovery and grade of 84.4 and 62.5%, respectively. In the absence of starch (depressant), PGA resulted in a separation efficiency of 56.1% compared to 43.7% without PGA. The PGA adsorption mechanism was mainly via physical interaction based on UV–vis spectra, zeta potential tests, Fourier transform infrared spectroscopy (FT-IR), and stability analyses. In general, the feasibility of using PGA, a natural green polymer, was beneficial for both grinding and reverse flotation separation performance.
Deduction learning for precise noninvasive measurements of blood glucose with a dozen rounds of data for model training
Authors: Lu Wei-Ru , Yang Wen-Tse , Chu Justin , Hsieh Tung-Han , Yang Fu-Liang 
Published: April 2022

AbstractPersonalized modeling has long been anticipated to approach precise noninvasive blood glucose measurements, but challenged by limited data for training personal model and its unavoidable outlier predictions. To overcome these long-standing problems, we largely enhanced the training efficiency with the limited personal data by an innovative Deduction Learning (DL), instead of the conventional Induction Learning (IL). The domain theory of our deductive method, DL, made use of accumulated comparison of paired inputs leading to corrections to preceded measured blood glucose to construct our deep neural network architecture. DL method involves the use of paired adjacent rounds of finger pulsation Photoplethysmography signal recordings as the input to a convolutional-neural-network (CNN) based deep learning model. Our study reveals that CNN filters of DL model generated extra and non-uniform feature patterns than that of IL models, which suggests DL is superior to IL in terms of learning efficiency under limited training data. Among 30 diabetic patients as our recruited volunteers, DL model achieved 80% of test prediction in zone A of Clarke Error Grid (CEG) for model training with 12 rounds of data, which was 20% improvement over IL method. Furthermore, we developed an automatic screening algorithm to delete low confidence outlier predictions. With only a dozen rounds of training data, DL with automatic screening achieved a correlation coefficient ($${R}_{P}$$RP) of 0.81, an accuracy score ($${R}_{A}$$RA) of 93.5, a root mean squared error of 13.93 mg/dl, a mean absolute error of 12.07 mg/dl, and 100% predictions in zone A of CEG. The nonparametric Wilcoxon paired test on$${R}_{A}$$RAfor DL versus IL revealed near significant difference withp-value 0.06. These significant improvements indicate that a very simple and precise noninvasive measurement of blood glucose concentration is achievable.
Variable-heavy (VH) families influencing IgA1&2 engagement to the antigen, FcαRI and superantigen proteins G, A, and L
Authors: Ling Wei-Li , Su Chinh Tran-To , Lua Wai-Heng , Yeo Joshua Yi , Poh Jun-Jie , Ng Yuen-Ling , Wipat Anil , Gan Samuel Ken-En 
Published: April 2022

AbstractInterest in IgA as an alternative antibody format has increased over the years with much remaining to be investigated in relation to interactions with immune cells. Considering the recent whole antibody investigations showing significant distal effects between the variable (V) and constant (C)- regions that can be mitigated by the hinge regions of both human IgA subtypes A1 and A2, we performed an in-depth mechanistic investigation using a panel of 28 IgA1s and A2s of both Trastuzumab and Pertuzumab models. FcαRI binding were found to be mitigated by the differing glycosylation patterns in IgA1 and 2 with contributions from the CDRs. On their interactions with antigen-Her2 and superantigens PpL, SpG and SpA, PpL was found to sterically hinder Her2 antigen binding with unexpected findings of IgAs binding SpG at the CH2-3 region alongside SpA interacting with IgAs at the CH1. Although the VH3 framework (FWR) is commonly used in CDR grafting, we found the VH1 framework (FWR) to be a possible alternative when grafting IgA1 and 2 owing to its stronger binding to antigen Her2 and weaker interactions to superantigen Protein L and A. These findings lay the foundation to understanding the interactions between IgAs and microbial superantigens, and also guide the engineering of IgAs for future antibody applications and targeting of superantigen-producing microbes.
Scientific investigation of a fractional model based on hybrid nanofluids with heat generation and porous medium: applications in the drilling process
Authors: Khan Dolat , Kumam Poom , Khan Ilyas , Khan Arshad , Watthayu Wiboonsak , Arif Muhammad 
Published: April 2022

AbstractThis article investigates graphite-aluminum oxide hybrid nanoparticles in water-base fluid with the addition of heat generation in the presence of a porous medium. The problem is formulated in terms of momentum and energy equations with sufficient initial and boundary conditions. The solution is investigated by using the Laplace transform method. It is observed that the velocity of the drilling fluid is controlled by adding hybrid nanoparticles as compared to simple nanofluids. In a similar way, the temperature of the fluid is reduced. Also, the heat transfer rate is boosted up to 37.40741% by using hybrid nanofluid compared to regular nanofluid. Moreover, the heat transfer rate was increased up to 11.149% by using different shapes of nanoparticles in the base fluid water. It is also observed that by using hybrid nanofluid skin fraction is boosted up at y = 0 and boosted down at y = 1.