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Azotobacter was selectively isolated and purified from the soil samples of Xinjiang Salt Lake Scenic spot, the fermentation technology of exopolysaccharides (EPS) by Azotobacter was optimized, and the antioxidant activity of exopolysaccharides (EPS) was studied. The bacteria were isolated and purified from the soil samples by the scribing method and the 16SrRNA gene was used for molecular identification. The carbon source, fermentation time, inoculation amount and pH of target bacteria in the exopolysaccharides (EPS) fermentation process were optimized through single-factor experiments and their antioxidant activity was measured. Eight types of Azotobacter were isolated and purified from the soil samples of Salt Lake scenic spot. Among them, As101, which showed 99.58% homology with Azotobacter salinestris, was selected as the target strain. Through single-factor experiments which used exopolysaccharides (EPS) yield and exopolysaccharides content as indexes, the optimal conditions for the As101 fermentation process were determined as follows: fermentation temperature 35, fermentation time 96h, pH 7 and mannitol as carbon source. Exopolysaccharides content from Azotobacter salinestris was 61.35% and the yield was 6.34 g/L. The results of the exopolysaccharides (EPS) antioxidant activity experiment under optimal conditions showed that As101 EPS had excellent scavenging ability against DPPH free radical, ABTS free radical and hydroxyl free radical, with IC50 values of 6.11 mg/ml, 2.42 mg/ml and 9.57 mg/ml, respectively. As101 with high yield and high exopolysaccharides content was isolated from saline soil in a special environment of Xinjiang, and the EPS obtained showed excellent antioxidant activity. The Azotobacter found in this study would provide the material basis for further opening up the adsorption of exopolysaccharides on heavy metals and the improvement of saline-alkali soil and contribute to further understanding of the structure and other activities of exopolysaccharides derived from Azotobacter.

Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a variety of options. In this manuscript, we developed hybrid associations models (HAM) to generate sequential recommendations using three factors: 1) users long-term preferences, 2) sequential, high-order and low-order association patterns in the users most recent purchases/ratings, and 3) synergies among those items. HAM uses simplistic pooling to represent a set of items in the associations, and element-wise product to represent item synergies of arbitrary orders. We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings. Our experimental results demonstrate that HAM models significantly outperform the state of the art in all the experimental settings, with an improvement as much as 46.6%. In addition, our run-time performance comparison in testing demonstrates that HAM models are much more efficient than the state-of-the-art methods, and are able to achieve significant speedup as much as 139.7 folds.

Reversed-phase liquid chromatography (RPLC) and capillary zone electrophoresis (CZE) are two popular proteoform separation methods in mass spectrometry (MS)-based top-down proteomics. The prediction of proteoform retention time in RPLC and migration time in CZE provides additional information that can increase the accuracy of proteoform identification and quantification. Whereas existing methods for retention and migration time prediction are mainly focused on peptides in bottom-up MS, there is still a lack of methods for the problem in top-down MS. We systematically evaluated 6 models for proteoform retention and/or migration time prediction in top-down MS and showed that the Prosit model achieved a high accuracy (R2 > 0.91) for proteoform retention time prediction and that the Prosit model and a fully connected neural network model obtained a high accuracy (R2 > 0.94) for proteoform migration time prediction.

T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (RL) problem, and presented a framework TCRPPO with a mutation policy using proximal policy optimization. TCRPPO mutates TCRs into effective ones that can recognize given peptides. TCRPPO leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared TCRPPO with multiple baseline methods and demonstrated that TCRPPO significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of TCRPPO for both precision immunotherapy and peptide-recognizing TCR motif discovery., 27th Annual International Conference, RECOMB 2023, April 16–19, 2023, Istanbul, Turkey, Series: Lecture Notes in Computer Science

Extracellular vesicles (EVs) have emerged as promising diagnostic and therapeutic candidates in many biomedical applications. However, EV research continues to rely heavily on in vitro cell cultures for EV production, where the exogenous EVs present in fetal bovine (FBS) or other required serum supplementation can be difficult to remove entirely. Despite this and other potential applications involving EV mixtures, there are currently no rapid, robust, inexpensive, and label-free methods for determining the relative concentrations of different EV subpopulations within a sample. In this study, we demonstrate that surface-enhanced Raman spectroscopy (SERS) can biochemically fingerprint fetal bovine serum-derived and bioreactor-produced EVs, and after applying a novel manifold learning technique to the acquired spectra, enables the quantitative detection of the relative amounts of different EV populations within an unknown sample. We first developed this method using known ratios of Rhodamine B to Rhodamine 6G, then using known ratios of FBS EVs to breast cancer EVs from a bioreactor culture. In addition to quantifying EV mixtures, the proposed deep learning architecture provides some knowledge discovery capabilities which we demonstrate by applying it to dynamic Raman spectra of a chemical milling process. This label-free characterization and analytical approach should translate well to other EV SERS applications, such as monitoring the integrity of semipermeable membranes within EV bioreactors, ensuring the quality or potency of diagnostic or therapeutic EVs, determining relative amounts of EVs produced in complex co-culture systems, as well as many Raman spectroscopy applications.

Motivation MHC Class I protein plays an important role in immunotherapy by presenting immunogenic peptides to anti-tumor immune cells. The repertoires of peptides for various MHC Class I proteins are distinct, which can be reflected by their diverse binding motifs. To characterize binding motifs for MHC Class I proteins, in vitro experiments have been conducted to screen peptides with high binding affinities to hundreds of given MHC Class I proteins. However, considering tens of thousands of known MHC Class I proteins, conducting in vitro experiments for extensive MHC proteins is infeasible, and thus a more efficient and scalable way to characterize binding motifs is needed. Results We presented a de novo generation framework, coined PepPPO, to characterize binding motif for any given MHC Class I proteins via generating repertoires of peptides presented by them. PepPPO leverages a reinforcement learning agent with a mutation policy to mutate random input peptides into positive presented ones. Using PepPPO, we characterized binding motifs for around 10 000 known human MHC Class I proteins with and without experimental data. These computed motifs demonstrated high similarities with those derived from experimental data. In addition, we found that the motifs could be used for the rapid screening of neoantigens at a much lower time cost than previous deep-learning methods. Availability and implementation The software can be found in https://github.com/minrq/pMHC. Supplementary information Supplementary data are available at Bioinformatics online.

Objective To characterize COVID-19 patients in Indiana, United States, and to evaluate their demographics and comorbidities as risk factors to COVID-19 severity. Materials and Methods EHR data of 776 936 COVID-19 cases and 1 362 545 controls were collected from the COVID-19 Research Data Commons (CoRDaCo) in Indiana. Data regarding county population and per capita income were obtained from the US Census Bureau. Statistical analysis was conducted to determine the association of demographic and clinical variables with COVID-19 severity. Predictive analysis was conducted to evaluate the predictive power of CoRDaCo EHR data in determining COVID-19 severity. Results Chronic obstructive pulmonary disease, cardiovascular disease, and type 2 diabetes were found in 3.49%, 2.59%, and 4.76% of the COVID-19 patients, respectively. Such COVID-19 patients have significantly higher ICU admission rates of 10.23%, 14.33%, and 11.11%, respectively, compared to the entire COVID-19 patient population (1.94%). Furthermore, patients with these comorbidities have significantly higher mortality rates compared to the entire COVID-19 patient population. Health disparity analysis suggests potential health disparities among counties in Indiana. Predictive analysis achieved F1-scores of 0.8011 and 0.7072 for classifying COVID-19 cases versus controls and ICU versus non-ICU cases, respectively. Discussion Black population in Indiana was more adversely affected by COVID-19 than the White population. This is consistent to findings from existing studies. Our findings also indicate other health disparities in terms of demographic and economic factors. Conclusion This study characterizes the relationship between comorbidities and COVID-19 outcomes with respect to ICU admission across a large COVID-19 patient population in Indiana.

Though rice proteins have been applied to improve the stability of phenolic compounds, the effects of rice proteins on the digestibility and bioavailability of phenolic acid have not been clear. This study devoted to understanding the effects of protein interaction with ferulic acid on the digestion and absorption of ferulic acid in gastrointestinal environment. Ferulic acid were formed complexes with rice proteins with and without the presence of laccase at room temperature. It was found that rice protein could protect ferulic acid from degradation in simulated oral fluid and remain stable in gastrointestinal fluids. With the hydrolysis of pepsin and pancreatin, rice protein-ferulic acid complexes degraded and released ferulic acid in gastrointestinal environment. The DPPH scavenging activity digested rice protein-ferulic acid complexed was maintained while that of digested ferulic acid was significantly decreased. Moreover, the permeability coefficient of ferulic acid was not affected by rice peptides. Thus, rice protein is a promising food matrix to protect ferulic acid in digestion tract and maintain the antioxidant functions of ferulic acid.

Molecule optimization is a critical step in drug development to improve desired properties of drug candidates through chemical modification. We developed a novel deep generative model Modof over molecular graphs for molecule optimization. Modof modifies a given molecule through the prediction of a single site of disconnection at the molecule and the removal and/or addition of fragments at that site. A pipeline of multiple, identical Modof models is implemented into Modof-pipe to modify an input molecule at multiple disconnection sites. Here we show that Modof-pipe is able to retain major molecular scaffolds, allow controls over intermediate optimization steps and better constrain molecule similarities. Modof-pipe outperforms the state-of-the-art methods on benchmark datasets: without molecular similarity constraints, Modof-pipe achieves 81.2% improvement in octanol-water partition coefficient penalized by synthetic accessibility and ring size; and 51.2%, 25.6% and 9.2% improvement if the optimized molecules are at least 0.2, 0.4 and 0.6 similar to those before optimization, respectively. Modof-pipe is further enhanced into Modof-pipem to allow modifying one molecule to multiple optimized ones. Modof-pipem achieves additional performance improvement as at least 17.8% better than Modof-pipe., This paper has been accepted by Nature Machine Intelligence

Objective Although vast amounts of patient information are captured in electronic health records (EHRs), effective clinical use of this information is challenging due to inadequate and inefficient access to it at the point of care. The purpose of this study was to conduct a scoping review of the literature on the use of EHR search functions within a single patient's record in clinical settings to characterize the current state of research on the topic and identify areas for future study. Methods We conducted a literature search of four databases to identify articles on within-EHR search functions or the use of EHR search function in the context of clinical tasks. After reviewing titles and abstracts and performing a full-text review of selected articles, we included 17 articles in the analysis. We qualitatively identified themes in those articles and synthesized the literature for each theme. Results Based on the 17 articles analyzed, we delineated four themes: (1) how clinicians use search functions, (2) impact of search functions on clinical workflow, (3) weaknesses of current search functions, and (4) advanced search features. Our review found that search functions generally facilitate patient information retrieval by clinicians and are positively received by users. However, existing search functions have weaknesses, such as yielding false negatives and false positives, which can decrease trust in the results, and requiring a high cognitive load to perform an inclusive search of a patient's record. Conclusion Despite the widespread adoption of EHRs, only a limited number of articles describe the use of EHR search functions in a clinical setting, despite evidence that they benefit clinician workflow and productivity. Some of the weaknesses of current search functions may be addressed by enhancing EHR search functions with collaborative filtering.

Landfill leachate have become a major public health concern because of their adverse effects on health. Due to its complex composition, the toxicological effects have yet to be evaluated. In this study, we use two model organisms: zebrafish and mung beans, to assess the toxic effects of landfill leachate. The results showed that low concentrations of waste leachate promoted the growth of mung beans, while high concentrations severely affected the growth and development of seedlings. Furthermore, landfill leachate caused a decrease in chlorophyll levels and malondialdehyde levels increased, significantly increased the rate of root tip micronuclei. In addition, zebrafish embryos exposed with 0.5%, 1%, 1.2%, 1.5% (v/v) landfill leachate, which was shown significantly reduced levels of embryonic incubation rate and heart rate, while the rates of mortality and malformation were increased. 1.0% of the landfill leachate in the experiment can result in a decrease in spontaneous movement frequency of embryos and the light stimulation reaction. The number of black and white area explore and mirror attacks were reduced. In general, these results help to understand the environmental toxicity of the landfill leachate, providing additional reference data for the risk assessment and management of landfill leachate.

Triazole fungicides have been widely used all over the world. However, their potential ecological safety and health risks remain unclear, especially their cardiac developmental toxicity. This study systematically investigated whether and how triazole fungicides could activate peroxisome proliferative activity receptor γ (PPARγ) to cause abnormal heart development. Among ten triazole fungicides, difenoconazole (DIF) exhibited the strongest agonistic activity and caused severe pericardial edema in zebrafish embryos, accompanied by a reduction in heart rate, blood flow and cardiac function. In vitro transcriptomic profile implicated that DIF inhibited the Wnt signaling pathway, and in vivo DIF exposure significantly increased the phosphorylation of β-catenin (p = 0.0002) and altered the expression of related genes in zebrafish embryos. Importantly, exposure to DIF could activate PPARγ and inhibit the Wnt/β-catenin signaling pathway, which changed the size of Kupffer's vesicle (KV) (p = 0.02), altered the expression of left-right (LR) asymmetry-related genes, caused cardiac LR asymmetry defect, and eventually led to abnormal heart development. These findings provide evidence for potential developmental toxicity of triazole fungicides and highlight the necessity of assessing their ecological safety and human health risks.

This article investigates using recommender systems within graphical domain-specific modeling languages (DSMLs). The objective of using recommender systems within a graphical DSML is to overcome a shortcoming of proactive modeling where the modeler must inform the model intelligence engine how to progress when it cannot automatically determine the next modeling action to execute (e.g., add, delete, or edit). To evaluate our objective, we implemented a recommender system into the Proactive Modeling Engine, which is an add-on for the Generic Modeling Environment. We then conducted experiments to subjectively and objectively evaluate enhancements to the Proactive Modeling Engine. The results of our experiments show that extending proactive modeling with a recommender system results in an average reciprocal hit-rank of 0.871. Likewise, the enhancements yield a System Usability Scale rating of 77. Finally, user feedback shows that integrating recommender systems into DSMLs increases usability and learnability.

Background Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer’s Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method We adapt a newly developed learning-to-rank approach $${\mathtt {PLTR}}$$ PLTR to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list. We also extend $${\mathtt {PLTR}}$$ PLTR to better separate the most effective cognitive assessments and the less effective ones. Results Our empirical study on the ADNI data shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing individual-specific cognitive biomarkers. We conduct experiments in cross validation and level-out validation settings. In the two settings, our paradigm significantly outperforms the best baselines with improvement as much as 22.1% and 19.7%, respectively, on prioritizing cognitive features. Conclusions The proposed paradigm achieves superior performance on prioritizing cognitive biomarkers. The cognitive biomarkers prioritized on top have great potentials to facilitate personalized diagnosis, disease subtyping, and ultimately precision medicine in AD.

Applied health informatics infrastructure is a requirement for learning health systems and it is imperative that we train a workforce that can support this infrastructure. Our department offers courses in several interdisciplinary programs with topics ranging from bioinformatics to population health informatics. Due to changes in the field and our faculty members, we sought to assess our courses relevant to applied health informatics.In this paper, we discuss the three-phase evaluation of our program and include the survey we developed to identify the skills and knowledge base of our faculty.We show how this assessment allowed us to identify gaps and develop strategies for program expansion.A focus on workforce development can help to guide and focus curricular review in an interdisciplinary graduate program.

Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate research on inferring knowledge from existing clinical trials in innovative ways for designing future clinical trials. In this manuscript, we present our efforts on constructing the first publicly available Clinical Trials Knowledge Graph, denoted as $$\mathop {\mathtt {CTKG}}\limits$$ CTKG . $$\mathop {\mathtt {CTKG}}\limits$$ CTKG includes nodes representing medical entities in clinical trials (e.g., studies, drugs and conditions), and edges representing the relations among these entities (e.g., drugs used in studies). Our embedding analysis demonstrates the potential utilities of $$\mathop {\mathtt {CTKG}}\limits$$ CTKG in various applications such as drug repurposing and similarity search, among others.

Top-down mass spectrometry has become the main method for intact proteoform identification, characterization, and quantitation. Because of the complexity of top-down mass spectrometry data, spectral deconvolution is an indispensable step in spectral data analysis, which groups spectral peaks into isotopic envelopes and extracts monoisotopic masses of precursor or fragment ions. The performance of spectral deconvolution methods relies heavily on their scoring functions, which distinguish correct envelopes from incorrect ones. A good scoring function increases the accuracy of deconvoluted masses reported from mass spectra. In this paper, we present EnvCNN, a convolutional neural network-based model for evaluating isotopic envelopes. We show that the model outperforms other scoring functions in distinguishing correct envelopes from incorrect ones and that it increases the number of identifications and improves the statistical significance of identifications in top-down spectral interpretation.

Supplemental material, sj-docx-1-tae-10.1177_20420188221106879 for Relationship between total testosterone, sex hormone–binding globulin levels and the severity of non-alcoholic fatty liver disease in males: a meta-analysis by Man-Qiu Mo, Zi-Chun Huang, Zhen-Hua Yang, Yun-Hua Liao, Ning Xia and Ling Pan in Therapeutic Advances in Endocrinology and Metabolism

Additional file 1.

Additional file 2.

Additional file 1: Fragment library generation and clustering algorithms.

Supplemental material, sj-docx-1-tae-10.1177_20420188221106879 for Relationship between total testosterone, sex hormone–binding globulin levels and the severity of non-alcoholic fatty liver disease in males: a meta-analysis by Man-Qiu Mo, Zi-Chun Huang, Zhen-Hua Yang, Yun-Hua Liao, Ning Xia and Ling Pan in Therapeutic Advances in Endocrinology and Metabolism

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Additional file 1.

In this work, we propose a new deep-learning model, MHCrank, to predict the probability that a peptide will be processed for presentation by MHC class I molecules. We find that the performance of our model is significantly higher than that of two previously published baseline methods: MHCflurry and netMHCpan. This improvement arises from utilizing both cleavage site-specific kernels and learned embeddings for amino acids. By visualizing site-specific amino acid enrichment patterns, we observe that MHCrank's top-ranked peptides exhibit enrichments at biologically relevant positions and are consistent with previous work. Furthermore, the cosine similarity matrix derived from MHCrank's learned embeddings for amino acids correlates highly with physiochemical properties that have been experimentally demonstrated to be instrumental in determining a peptide's favorability for processing. Altogether, the results reported in this work indicate that MHCrank demonstrates strong performance compared with existing methods and could have vast applicability in aiding drug and vaccine development.

Recent advances in molecular machine learning, especially deep neural networks such as Graph Neural Networks (GNNs) for predicting structure activity relationships (SAR) have shown tremendous potential in computer-aided drug discovery. However, the applicability of such deep neural networks are limited by the requirement of large amounts of training data. In order to cope with limited training data for a target task, transfer learning for SAR modeling has been recently adopted to leverage information from data of related tasks. In this work, in contrast to the popular parameter-based transfer learning such as pretraining, we develop novel deep transfer learning methods TAc and TAc-fc to leverage source domain data and transfer useful information to the target domain. TAc learns to generate effective molecular features that can generalize well from one domain to another, and increase the classification performance in the target domain. Additionally, TAc-fc extends TAc by incorporating novel components to selectively learn feature-wise and compound-wise transferability. We used the bioassay screening data from PubChem, and identified 120 pairs of bioassays such that the active compounds in each pair are more similar to each other compared to its inactive compounds. Our experiments clearly demonstrate that TAc achieves significant improvement over all baselines across a large number of target tasks. Furthermore, although TAc-fc achieves slightly worse ROC-AUC on average compared to TAc, TAc-fc still achieves the best performance on more tasks in terms of PR-AUC and F1 compared to other methods. In summary, TAc-fc is also found to be a strong model with competitive or even better performance than TAc on a notable number of target tasks., This manuscript has been accepted at ACS Omega

Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate research on inferring knowledge from existing clinical trials in innovative ways for designing future clinical trials. In this manuscript, we present our efforts on constructing the first publicly available Clinical Trials Knowledge Graph, denoted as CTKG. CTKG includes nodes representing medical entities in clinical trials (e.g., studies, drugs and conditions), and edges representing the relations among these entities (e.g., drugs used in studies). Our embedding analysis demonstrates the potential utilities of CTKG in various applications such as drug repurposing and similarity search, among others.

Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate research on inferring knowledge from existing clinical trials in innovative ways for designing future clinical trials. In this manuscript, we present our efforts on constructing the first publicly available Clinical Trials Knowledge Graph, denoted as [Formula: see text]. [Formula: see text] includes nodes representing medical entities in clinical trials (e.g., studies, drugs and conditions), and edges representing the relations among these entities (e.g., drugs used in studies). Our embedding analysis demonstrates the potential utilities of [Formula: see text] in various applications such as drug repurposing and similarity search, among others.

In this work, we propose a new deep learning model, MHCrank, to predict the probability that a peptide will be processed for presentation within the MHC Class I pathway. We find that the performance of our model is significantly higher than two previously published baseline methods: MHCflurry and netMHCpan. Gains in performance result from the utilization of cleavage site-specific kernels and learned representations for amino acids. By visualizing the site-specific amino acid enrichment among top-ranked peptides, we find MHCrank’s top-ranked peptides are enriched at biologically relevant positions with amino acids that are consistent with previous work. Furthermore, the cosine similarity matrix derived from MHCrank’s learned embeddings for amino acids correlate highly with physiochemical properties that have been experimentally shown to be important in determining a peptide’s favorability to be processed. Altogether, the results reported in this work indicate that the proposed MHCrank demonstrates strong performance compared to existing methods and could have vast applicability to aid drug and vaccine development.

BackgroundLean body mass (LBM) composition of cancer patients is a predictor of chemotherapy-related adverse events and overall cancer survival. However, clinicians lack validated algorithms that can be applied to measure the LBM of cancer patients to facilitate accurate chemotherapy dosing. Our goal was to develop LBM predictive equations using routinely measured anthropometric measures among cancer patients.MethodsWe leveraged the 1999-2006 National Health and Nutrition Examination Survey (NHANES) data cycles containing information on self-reported cancer diagnosis, LBM measures based on dual-energy x-ray absorptiometry (DXA) and several anthropometric and demographic factors. We restricted our analysis to participants who had been diagnosed with cancer at the time of surveys. The data was randomly split to 75%:25% to train and test predictive models. Least absolute shrinkage and selection operator (LASSO) models were used to predict LBM based on anthropometric and demographic factors, overall and separately among sex and sex-by-race/ethnic subgroups. LBM measured directly with DXA served as the gold standard for assessing the predictive abilities (correlations [R2] and the Root Mean Square Error [RMSE]) of the derived LBM-algorithms. We further compared the correlations between both DXA-based LBM and predicted LBM and urine creatinine levels, a known biomarker of muscle mass.ResultsWe identified 1,777 cancer patients with a median age of 71 (interquartile range [IQR]: 60-80) years. The most parsimonious model comprised of height and weight, which accurately predicted LBM overall (R2=0.86, RMSE =2.26). The predictive abilities of these models varied across sex-by-race/ethnic groups. The magnitude of correlations between derived LBM-algorithm and urine creatinine levels were larger compared to those measured between DXA-based LBM and urine creatinine levels (R2=0.30 vs. R2=0.17)ConclusionsWe successfully developed a simple sex-specific and sex-by-race/ethnicity-specific models to accurately predict the LBM of cancer patients by using only height and weight. The simplicity and high accuracy of these models make them inexpensive alternatives to measuring the LBM of cancer patients. Data on the LBM of cancer patients could help guide optimal chemotherapy dose selection among cancer patients.

In this study, we analysed the intracellular fatty acid profiles of Komagataella phaffii during methylotrophic growth. K. phaffii grown on methanol had significantly lower total fatty acid contents in the cells compared with glucose-grown cells. C18 and C16 fatty acids were the predominant fatty acids in K. phaffii, although the contents of odd-chain fatty acids such as C17 fatty acids were also relatively high. Moreover, the intracellular fatty acid composition of K. phaffii changed in response to not only carbon sources but also methanol concentrations: C17 fatty acids and C18:2 content increased significantly as methanol concentration increased, whereas C18:1 and C18:3 contents were significantly lower in methanol-grown cells. The intracellular content of unidentified compounds (Cn H2n O4 ), on the other hand, was significantly greater in cells grown on methanol. As the intracellular contents of these Cn H2n O4 compounds were significantly higher in a gene-disrupted strain for glutathione peroxidase (gpx1Δ) than in the wild-type strain, we presume that the Cn H2n O4 compounds are fatty acid peroxides. These results indicate that K. phaffii can coordinate intracellular fatty acid composition during methylotrophic growth in order to adapt to high-methanol conditions and that certain fatty acid species such as C17:0, C17:1, C17:2 and C18:2 may be related to the physiological functions by which K. phaffii adapts to high-methanol conditions.

Triplophysa yarkandensis is a specific cobitidae species that is endemic to Xinjiang Tarim River basin, China. The complete mitochondrial genome sequence of T. yarkandensis from Bosten Lake was determined in this study (Accession number MN821008). The mitogenome (16,552 bp) consists of 22 tRNA genes, 2 ribosomal RNA genes, 13 protein-coding genes, and 1 control region (D-loop region). The complete mitochondrial genome sequence of the T. yarkandensis provides an important data set for further study in genetic mechanism and classification.

A solar mid-temperature evacuated tube collector, which was applied in a new seawater desalination system with multi-stage evaporation and recovery processes, was designed and tested. This collector can be viewed as a mid-temperature steam generator for providing steady steam with a temperature exceeding 130 °C. The collector can harvest solar energy only by improved all-glass evacuated tubes with low emissivity, and there is no need for solar concentrator that was applied in most mid-temperature solar collector. Therefore, a lot of cost can be saved and the system was low-cost. A series of experiments were carried out to investigate the effects of operation parameters including steam temperature, solar irradiance, weather conditions and volume of steam drums on the operation performance of the solar collector. The experimental results confirmed that the solar collector has an excellent collecting efficiency and can supply sufficient mid-temperature steam for a long time. This research proposed a low-cost solar collector for steam generator and lay a foundation for the further study on the entire seawater desalination system.

Ambient fine particulate matter (PM2.5) is a challenge to public health worldwide. Although increasing numbers of recent epidemiological studies have emphasized the critical role of PM2.5 in promoting respiratory diseases, the precise mechanism behind PM2.5-mediated lung obstruction remains obscure. In the present study, we analyzed lung structure and function and further investigated mitochondrial morphology and transcription-modulated energy metabolism in mice following PM2.5 aspiration. The results showed that PM2.5 exposure reduced pulmonary function and induced severe pathological alterations, including alveolar endothelial disruption and airway obstruction. Based on ultrastructural observations, we also found mitochondrial vacuolation and mitochondrial membrane rupture in alveolar type II epithelial cells. Importantly, the abnormality of mitochondrial structure was coupled with energy metabolism disorders, as evidenced by the decrease in ATP levels, the accumulation of pyruvate and lactate content, and the altered transcription of related genes. Moreover, the reduction in mitochondrial markers, including PGC-1α, NRF-1, and TFAM, were involved in mitochondrial dysfunction. These findings suggest that energy metabolic disorders and mitochondrial dysfunction may be the important contributors to pulmonary injuries in response to PM2.5 exposure, indicating possible targets for protection and therapy in polluted areas.

Electronic health record (EHR) data are increasingly used to develop prediction models to support clinical care, including the care of patients with common chronic conditions. A key challenge for individual healthcare systems in developing such models is that they may not be able to achieve the desired degree of robustness using only their own data. A potential solution-combining data from multiple sources-faces barriers such as the need for data normalization and concerns about sharing patient information across institutions. To address these challenges, we evaluated three alternative approaches to using EHR data from multiple healthcare systems in predicting the outcome of pharmacotherapy for type 2 diabetes mellitus(T2DM). Two of the three approaches, named Selecting Better (SB) and Weighted Average(WA), allowed the data to remain within institutional boundaries by using pre-built prediction models; the third, named Combining Data (CD), aggregated raw patient data into a single dataset. The prediction performance and prediction coverage of the resulting models were compared to single-institution models to help judge the relative value of adding external data and to determine the best method to generate optimal models for clinical decision support. The results showed that models using WA and CD achieved higher prediction performance than single-institution models for common treatment patterns. CD outperformed the other two approaches in prediction coverage, which we defined as the number of treatment patterns predicted with an Area Under Curve of 0.70 or more. We concluded that 1) WA is an effective option for improving prediction performance for common treatment patterns when data cannot be shared across institutional boundaries and 2) CD is the most effective approach when such sharing is possible, especially for increasing the range of treatment patterns that can be predicted to support clinical decision making.