The Prognostic Value of D-Dimer in Patients with Acute Myocardial Infarction: A Retrospective Longitudinal Cohort Study in Taiwan.

Background: Serum D-dimer level has been associated with worsening outcomes in patients with acute myocardial infarction. This study aimed to explore the association between serum D-dimer level and clinical outcomes in Taiwanese patients with acute myocardial infarction. Methods: We analyzed Tri-Service General Hospital-Coronary Heart Disease registry data related to patients with acute myocardial infarction who were admitted between January 2014 and December 2018. A total of 748 patients were enrolled and categorized into high (≥ 495 ng/ml) and low (< 495 ng/ml) D-dimer groups. The primary endpoint was in-hospital mortality, and secondary endpoints were post-discharge mortality and post-discharge major adverse cardiovascular events. Results: Overall, 139 patients died, with 77 from cardiovascular causes and 62 from non-cardiovascular causes. In-hospital mortality was higher in the high D-dimer group than in the low D-dimer group. Among the patients alive at discharge, those with a high D-dimer level had higher cardiovascular mortality and future major adverse cardiovascular events than those with a low D-dimer level. Multivariate Cox regression analysis revealed that higher serum D-dimer levels were significantly associated with higher risks of in-hospital mortality [hazard ratio (HR) = 1.11; 95% confidence interval (CI), 1.06-1.16, p < 0.001], subsequent cardiovascular mortality after discharge (HR = 1.15; 95% CI, 1.08-1.22, p < 0.001), and major adverse cardiovascular events (HR = 1.10; 95% CI, 1.04-1.16, p < 0.001). Conclusions: This is the first study in Taiwan to demonstrate that a higher baseline serum D-dimer level was independently associated with higher risks of in-hospital mortality, post-discharge mortality, and major adverse cardiovascular events in patients with acute myocardial infarction.

Chronic restraint stress induces depression-like behaviors and alterations in the afferent projections of medial prefrontal cortex from multiple brain regions in mice.

INTRODUCTION: The medial prefrontal cortex (mPFC) forms output pathways through projection neurons, inversely receiving adjacent and long-range inputs from other brain regions. However, how afferent neurons of mPFC are affected by chronic stress needs to be clarified. In this study, the effects of chronic restraint stress (CRS) on the distribution density of mPFC dendrites/dendritic spines and the projections from the cortex and subcortical brain regions to the mPFC were investigated. METHODS: In the present study, C57BL/6 J transgenic (Thy1-YFP-H) mice were subjected to CRS to establish an animal model of depression. The infralimbic (IL) of mPFC was selected as the injection site of retrograde AAV using stereotactic technique. The effects of CRS on dendrites/dendritic spines and afferent neurons of the mPFC IL were investigaed by quantitatively assessing the distribution density of green fluorescent (YFP) positive dendrites/dendritic spines and red fluorescent (retrograde AAV recombinant protein) positive neurons, respectively. RESULTS: The results revealed that retrograde tracing virus labeled neurons were widely distributed in ipsilateral and contralateral cingulate cortex (Cg1), second cingulate cortex (Cg2), prelimbic cortex (PrL), infralimbic cortex, medial orbital cortex (MO), and dorsal peduncular cortex (DP). The effects of CRS on the distribution density of mPFC red fluorescence positive neurons exhibited regional differences, ranging from rostral to caudal or from top to bottom. Simultaneously, CRS resulted a decrease in the distribution density of basal, proximal and distal dendrites, as well as an increase in the loss of dendritic spines of the distal dendrites in the IL of mPFC. Furthermore, varying degrees of red retrograde tracing virus fluorescence signals were observed in other cortices, amygdala, hippocampus, septum/basal forebrain, hypothalamus, thalamus, mesencephalon, and brainstem in both ipsilateral and contralateral brain. CRS significantly reduced the distribution density of red fluorescence positive neurons in other cortices, hippocampus, septum/basal forebrain, hypothalamus, and thalamus. Conversely, CRS significantly increased the distribution density of red fluorescence positive neurons in amygdala. CONCLUSION: Our results suggest a possible mechanism that CRS leads to disturbances in synaptic plasticity by affecting multiple inputs to the mPFC, which is characterized by a decrease in the distribution density of dendrites/dendritic spines in the IL of mPFC and a reduction in input neurons of multiple cortices to the IL of mPFC as well as an increase in input neurons of amygdala to the IL of mPFC, ultimately causing depression-like behaviors.

Renal interstitial fibrotic assessment using non-Gaussian diffusion kurtosis imaging in a rat model of hyperuricemia.

BACKGROUND: To investigate the feasibility of Diffusion Kurtosis Imaging (DKI) in assessing renal interstitial fibrosis induced by hyperuricemia. METHODS: A hyperuricemia rat model was established, and the rats were randomly split into the hyperuricemia (HUA), allopurinol (AP), and AP + empagliflozin (AP + EM) groups (n = 19 per group). Also, the normal rats were selected as controls (CON, n = 19). DKI was performed before treatment (baseline) and on days 1, 3, 5, 7, and 9 days after treatment. The DKI indicators, including mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) of the cortex (CO), outer stripe of the outer medulla (OS), and inner stripe of the outer medulla (IS) were acquired. Additionally, hematoxylin and eosin (H&E) staining, Masson trichrome staining, and nuclear factor kappa B (NF-κB) immunostaining were used to reveal renal histopathological changes at baseline, 1, 5, and 9 days after treatment. RESULTS: The HUA, AP, and AP + EM group MKOS and MKIS values gradually increased during this study. The HUA group exhibited the highest MK value in outer medulla. Except for the CON group, all the groups showed a decreasing trend in the FA and MD values of outer medulla. The HUA group exhibited the lowest FA and MD values. The MKOS and MKIS values were positively correlated with Masson's trichrome staining results (r = 0.687, P < 0.001 and r = 0.604, P = 0.001, respectively). The MDOS and FAIS were negatively correlated with Masson's trichrome staining (r = -626, P < 0.0014 and r = -0.468, P = 0.01, respectively). CONCLUSION: DKI may be a non-invasive method for monitoring renal interstitial fibrosis induced by hyperuricemia.

[Characteristics, Sources Apportionment, and Health Risks of PM2.5-bound PAHs and Their Derivatives Before and After Heating in Zibo City].

Atmospheric polycyclic aromatic hydrocarbons (PAHs) and their derivatives are a global problem that influences the environment and threatens human health. To investigate the characteristics, sources, and health risk assessment of PM2.5-bound PAHs and their derivatives, PM2.5 were collected at an urban site in Zibo from November 5 to December 26, 2020, and the concentrations of 16 conventional PAHs, nine NPAHs, and five OPAHs in PM2.5 were analyzed using gas chromatography-mass spectrometry. Source apportionment of PAHs and their derivatives was conducted using diagnostic ratios and a PMF model, and the health risks of PAHs and their derivatives to adult men and women were evaluated using the source-dependent incremental lifetime cancer risk (ILCR) model. The results showed that the average concentrations of ∑16pPAHs, ∑9NPAHs, and ∑5OPAHs in PM2.5 of Zibo City during the sampling period were (41.61 ± 13.40), (6.38 ± 5.70), and (53.20 ± 53.47) ng·m-3, respectively. The concentrations of the three PAHs increased significantly after heating, which were 1.31, 2.04, and 5.24 times larger than those before heating. During the sampling period, Chr, BaP, and BaA were the dominant components of pPAHs; 9N-Ant and 2N-Flt + 3N-Flt were the dominant components of NPAHs; and ATQ and BZO were the dominant components of OPAHs. Source apportionment results showed that motor vehicles were the main source of PAHs and their derivatives in PM2.5 before heating, whereas after heating, the main sources were the mixed source of coal and biomass combustion and secondary formation. The total BaP equivalent (TEQ) was 14.5 ng·m-3 during the sampling period, and the TEQ increased significantly after heating, which was approximately 1.2 times of that before heating. Assisted by the individual PAH source apportionment results, the ILCR of PM2.5-boundPAHs and NPAHs in Zibo City had a certain potential carcinogenic risk for adult males (1.06 × 10-5) and females (9.32 × 10-6). Among them, the health risks of PAHs from gasoline vehicles, diesel vehicles, and coal/biomass combustion were significantly higher than those from other emission sources.

Research on the key technology of high-precision waveform synchronization identification and measurement.

High-precision waveform identification and measurement are effective for waveform detection and evaluation in signal processing. The accuracy of waveform identification, precision of measurement, and speed of response are important indicators of waveform measurement instruments. To detect the waveform accurately, a hold and attenuation circuit divided into two is designed, and the STM32F4 microcontroller is used to accurately capture and perform spectrum analysis using a high-precision analog-to-digital converter based on fast Fourier transform technology to identify key parameters, such as waveform type, frequency, peak-to-peak value, and duty cycle. To improve the recognition accuracy and response speed, technical solutions, such as high-frequency sampling and over-zero detection, are used to improve the system efficiency. Algorithm simulation, circuit simulation, and physical testing show that the high-precision waveform synchronization recognition circuit and algorithm can accurately recognize various essential waveforms in the voltage and frequency ranges of 50 mV ≤ VPP ≤ 10 V and 1 Hz ≤ f ≤ 50 kHz, respectively, and simultaneously measure important parameters, such as frequency, peak-to-peak value, and duty cycle with an accuracy within ±1%. Intelligent linkage, no intermediate parameter setting, and a response speed of approximately 0.3 s make it suitable for such applications as fast and high-precision waveform intelligent detection and display. The method is highly integrated, simple to operate, cost-effective, and practical.

Using intravoxel incoherent motion imaging to evaluate uric acid-induced renal injury and efficacy after treatment.

OBJECTIVES: To validate the feasibility of intravoxel incoherent motion imaging (IVIM) for monitoring renal injury and uric acid-lowering efficacy in a rat model of hyperuricaemia. METHODS: A total of 92 rats were analysed and categorized into 4 groups: control (CON), hyperuricaemia (HUA), allopurinol intervention (ALL), and combined intervention (COM). Eight rats were randomly selected from each group and underwent IVIM scanning on days 0, 1, 3, 5, 7, and 9. Quantitative magnetic resonance values (D, D*, and f values) measured from the different renal anatomical regions. Quantitative histopathological analysis was performed to assess renal tubular injury using neutrophil gelatinase-associated lipocalin (NGAL), and renal fibrosis using alpha-smooth-muscle-actin (α-SMA). Pearson's correlation analysis was used to determine the correlation between IVIM-derived parameters and the expression of NGAL and α-SMA. RESULTS: The D values of the HUA, ALL, and COM groups generally showed a downward trend over time, and this fluctuation was most significant in the HUA group. The D values showed significant intergroup differences at each point, whereas only a few discrepancies were found in the D* and f values. In addition, the renal D value was negatively correlated with the positive staining rates for NGAL and α-SMA (P < .05), except for the lack of correlation between Dos and α-SMA (P > .05). CONCLUSION: IVIM could be a noninvasive and potential assessment modality for the evaluation of renal injury induced by hyperuricaemia and its prognostic efficacy. ADVANCES IN KNOWLEDGE: IVIM could be a surrogate manner in monitoring renal damage induced by hyperuricaemia and its treatment evaluation.

CMAUP database update 2024: extended functional and association information of useful plants for biomedical research.

Knowledge of the collective activities of individual plants together with the derived clinical effects and targeted disease associations is useful for plant-based biomedical research. To provide the information in complement to the established databases, we introduced a major update of CMAUP database, previously featured in NAR. This update includes (i) human transcriptomic changes overlapping with 1152 targets of 5765 individual plants, covering 74 diseases from 20 027 patient samples; (ii) clinical information for 185 individual plants in 691 clinical trials; (iii) drug development information for 4694 drug-producing plants with metabolites developed into approved or clinical trial drugs; (iv) plant and human disease associations (428 737 associations by target, 220 935 reversion of transcriptomic changes, 764 and 154121 associations by clinical trials of individual plants and plant ingredients); (v) the location of individual plants in the phylogenetic tree for navigating taxonomic neighbors, (vi) DNA barcodes of 3949 plants, (vii) predicted human oral bioavailability of plant ingredients by the established SwissADME and HobPre algorithm, (viii) 21-107% increase of CMAUP data over the previous version to cover 60 222 chemical ingredients, 7865 plants, 758 targets, 1399 diseases, 238 KEGG human pathways, 3013 gene ontologies and 1203 disease ontologies. CMAUP update version is freely accessible at https://bidd.group/CMAUP/index.html.

Exploring outer space biophysical phenomena via SpaceLID.

Extensive investigations in outer space have revealed not only how life adapts to the space environment, but also that interesting biophysical phenomena occur. These phenomena affect human health and other life forms (animals, plants, bacteria, and fungi), and to ensure the safety of future human space exploration need to be further investigated. This calls for joint research efforts between biologists and physicists, as these phenomena present cross-disciplinary barriers. Various national organizations provide useful forums for bridging this gap. Additional discussion avenues and database resources are helpful for facilitating the interdisciplinary investigations of these phenomena. In this paper, we present the newly established Space Life Investigation Database (SpaceLID, https://bidd.group/spacelid/ ) which provides information about biophysical phenomena occurring in space. Examples obtained using the database are given while discussing the underlying causes of these phenomena and their implications for the physiology and health of life in space.

Free-Space and Fiber-Integrated Measurement-Device-Independent Quantum Key Distribution under High Background Noise.

Measurement-device-independent quantum key distribution (MDI QKD) provides immunity against all attacks targeting measurement devices. It is essential to implement MDI QKD in the future global-scale quantum communication network. Toward this goal, we demonstrate a robust MDI QKD fully covering daytime, overcoming the high background noise that prevents BB84 protocol even when using a perfect single-photon source. Based on this, we establish a hybrid quantum communication network that integrates free-space and fiber channels through Hong-Ou-Mandle (HOM) interference. Additionally, we investigate the feasibility of implementing HOM interference with moving satellites. Our results serve as a significant cornerstone for future integrated space-ground quantum communication networks that incorporate measurement-device-independent security.

Effects of Compound Danshen Dripping Pills on Ventricular Remodeling and Cardiac Function after Acute Anterior Wall ST-Segment Elevation Myocardial Infarction (CODE-AAMI): Protocol for a Randomized Placebo-Controlled Trial.

BACKGROUND: Ventricular remodeling after acute anterior wall ST-segment elevation myocardial infarction (AAMI) is an important factor in occurrence of heart failure which additionally results in poor prognosis. Therefore, the treatment of ventricular remodeling needs to be further optimized. Compound Danshen Dripping Pills (CDDP), a traditional Chinese medicine, exerts a protective effect on microcirculatory disturbance caused by ischemia-reperfusion injury and attenuates ventricular remodeling after myocardial infarction. OBJECTIVE: This study is designed to evaluate the efficacy and safety of CDDP in improving ventricular remodeling and cardiac function after AAMI on a larger scale. METHODS: This study is a multi-center, randomized, double-blind, placebo-controlled, parallel-group clinical trial. The total of 268 patients with AAMI after primary percutaneous coronary intervention (pPCI) will be randomly assigned 1:1 to the CDDP group (n=134) and control group (n=134) with a follow-up of 48 weeks. Both groups will be treated with standard therapy of ST-segment elevation myocardial infarction (STEMI), with the CDDP group administrating 20 tablets of CDDP before pPCI and 10 tablets 3 times daily after pPCI, and the control group treated with a placebo simultaneously. The primary endpoint is 48-week echocardiographic outcomes including left ventricular ejection fraction (LVEF), left ventricular end-diastolic volume index (LVEDVI), and left ventricular end-systolic volume index (LVESVI). The secondary endpoint includes the change in N terminal pro-B-type natriuretic peptide (NT-proBNP) level, arrhythmias, and cardiovascular events (death, cardiac arrest, or cardiopulmonary resuscitation, rehospitalization due to heart failure or angina pectoris, deterioration of cardiac function, and stroke). Investigators and patients are both blinded to the allocated treatment. DISCUSSION: This prospective study will investigate the efficacy and safety of CDDP in improving ventricular remodeling and cardiac function in patients undergoing pPCI for a first AAMI. Patients in the CDDP group will be compared with those in the control group. If certified to be effective, CDDP treatment in AAMI will probably be advised on a larger scale. (Trial registration No. NCT05000411).

Database of space life investigations and information on spaceflight plant biology.

Extensive spaceflight life investigations (SLIs) have revealed observable space effects on plants, particularly their growth, nutrition yield, and secondary metabolite production. Knowledge of these effects not only facilitates space agricultural and biopharmaceutical technology development but also provides unique perspectives to ground-based investigations. SLIs are specialized experimental protocols and notable biological phenomena. These require specialized databases, leading to the development of the NASA Science Data Archive, Erasmus Experiment Archive, and NASA GeneLab. The increasing interests of SLIs across diverse fields demand resources with comprehensive content, convenient search facilities, and friendly information presentation. A new database SpaceLID (Space Life Investigation Database http://bidd.group/spacelid/ ) was developed with detailed menu search tools and categorized contents about the phenomena, protocols, and outcomes of 459 SLIs (including 106 plant investigations) of 92 species, where 236 SLIs and 57 plant investigations are uncovered by the existing databases. The usefulness of SpaceLID as an SLI information source is illustrated by the literature-reported analysis of metabolite, nutrition, and symbiosis variations of spaceflight plants. In conclusion, this study extensively investigated the impact of the space environment on plant biology, utilizing SpaceLID as an information source and examining various plant species, including Arabidopsis thaliana, Brassica rapa L., and Glycyrrhiza uralensis Fisch. The findings provide valuable insights into the effects of space conditions on plant physiology and metabolism.

Infrared Spectral Analysis for Prediction of Functional Groups Based on Feature-Aggregated Deep Learning.

Infrared (IR) spectroscopy is a powerful and versatile tool for analyzing functional groups in organic compounds. A complex and time-consuming interpretation of massive unknown spectra usually requires knowledge of chemistry and spectroscopy. This paper presents a new deep learning method for transforming IR spectral features into intuitive imagelike feature maps and prediction of major functional groups. We obtained 8272 gas-phase IR spectra from the NIST Chemistry WebBook. Feature maps are constructed using the intrinsic correlation of spectral data, and prediction models are developed based on convolutional neural networks. Twenty-one major functional groups for each molecule are successfully identified using binary and multilabel models without expert guidance and feature selection. The multilabel classification model can produce all prediction results simultaneously for rapid characterization. Further analysis of the detailed substructures indicates that our model is capable of obtaining abundant structural information from IR spectra for a comprehensive investigation. The interpretation of our model reveals that the peaks of most interest are similar to those often considered by spectroscopists. In addition to demonstrating great potential for spectral identification, our method may contribute to the development of automated analyses in many fields.

Transcriptome and metabolome analyses reveal gender-specific expression genes in sea cucumber (Holothuria leucospilota).

The sea cucumber Holothuria leucospilota, a nutritive and commercial marine species, has a high protein and low lipid content. To date, the mechanisms underlying gender determination and differentiation in sea cucumbers remain unclear. Identifying gender-specific molecular markers is an effective method of revealing the genetic basis of gender determination and differentiation. The inability to distinguish between male and female individuals causes reproductive efficiency to decline in aquaculture. In this study, we used the gonads of the sea cucumber H. leucospilota as samples to conduct the experiment. The differentially abundant metabolites (DAMs) detected by liquid chromatography-mass spectrometry were enriched in pathways associated with prolactin metabolism, insulin metabolism, hypoxia-inducible factor-1 signaling, and calcium signaling. At the transcriptome level, Illumina sequencing was performed on H. leucospilota, demonstrating that gender-specific expression genes were enriched in the retinoic acid-inducible gene I-like receptor signaling pathway, C-type lectin receptor signaling pathway, alpha-linolenic acid metabolism, and ether lipid metabolism by the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. By analyzing the common pathways between DAMs and differentially expressed genes, we found that gender-related genes of H. leucospilota were mostly enriched in the necroptosis pathway and the cysteine and methionine metabolism pathways. According to the common pathways, uch-sc1 and uch-sc2 are male-specific expression genes, and uch-sc3 and bhmt are female-specific expression genes at the mRNA level. These results provide information on gender differences in H. leucospilota.

Deep learning of 2D-Restructured gene expression representations for improved low-sample therapeutic response prediction.

Clinical outcome prediction is important for stratified therapeutics. Machine learning (ML) and deep learning (DL) methods facilitate therapeutic response prediction from transcriptomic profiles of cells and clinical samples. Clinical transcriptomic DL is challenged by the low-sample sizes (34-286 subjects), high-dimensionality (up to 21,653 genes) and unordered nature of clinical transcriptomic data. The established methods rely on ML algorithms at accuracy levels of 0.6-0.8 AUC/ACC values. Low-sample DL algorithms are needed for enhanced prediction capability. Here, an unsupervised manifold-guided algorithm was employed for restructuring transcriptomic data into ordered image-like 2D-representations, followed by efficient DL of these 2D-representations with deep ConvNets. Our DL models significantly outperformed the state-of-the-art (SOTA) ML models on 82% of 17 low-sample benchmark datasets (53% with >0.05 AUC/ACC improvement). They are more robust than the SOTA models in cross-cohort prediction tasks, and in identifying robust biomarkers and response-dependent variational patterns consistent with experimental indications.

Robust twin-field quantum key distribution through sending or not sending.

The sending-or-not-sending (SNS) protocol is one of the most major variants of the twin-field (TF) quantum key distribution (QKD) protocol and has been realized in a 511-km field fiber, the farthest field experiment to date. In practice, however, all decoy-state methods have unavoidable source errors, and the source errors may be non-random, which compromises the security condition of the existing TF-QKD protocols. In this study, we present a general approach for efficiently calculating the SNS protocol's secure key rate with source errors, by establishing the equivalent protocols through virtual attenuation and the tagged model. This makes the first result for TF QKD in practice where source intensity cannot be controlled exactly. Our method can be combined with the two-way classical communication method such as active odd-parity pairing to further improve the key rate. The numerical results show that if the intensity error is within a few percent, the key rate and secure distance only decrease marginally. The key rate of the recent SNS experiment in the 511-km field fiber is still positive using our method presented here, even if there is a [Formula: see text] intensity fluctuation. This shows that the SNS protocol is robust against source errors.

NPASS database update 2023: quantitative natural product activity and species source database for biomedical research.

Quantitative activity and species source data of natural products (NPs) are important for drug discovery, medicinal plant research, and microbial investigations. Activity values of NPs against specific targets are useful for discovering targeted therapeutic agents and investigating the mechanism of medicinal plants. Composition/concentration values of NPs in individual species facilitate the assessments and investigations of the therapeutic quality of herbs and phenotypes of microbes. Here, we describe an update of the NPASS natural product activity and species source database previously featured in NAR. This update includes: (i) new data of ∼95 000 records of the composition/concentration values of ∼1 490 NPs/NP clusters in ∼390 species, (ii) extended data of activity values of ∼43 200 NPs against ∼7 700 targets (∼40% and ∼32% increase, respectively), (iii) extended data of ∼31 600 species sources of ∼94 400 NPs (∼26% and ∼32% increase, respectively), (iv) new species types of ∼440 co-cultured microbes and ∼420 engineered microbes, (v) new data of ∼66 600 NPs without experimental activity values but with estimated activity profiles from the established chemical similarity tool Chemical Checker, (vi) new data of the computed drug-likeness properties and the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties for all NPs. NPASS update version is freely accessible at http://bidd.group/NPASS.

Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations.

Metagenomic analysis has been explored for disease diagnosis and biomarker discovery. Low sample sizes, high dimensionality, and sparsity of metagenomic data challenge metagenomic investigations. Here, an unsupervised microbial embedding, grouping, and mapping algorithm (MEGMA) was developed to transform metagenomic data into individualized multichannel microbiome 2D representation by manifold learning and clustering of microbial profiles (e.g., composition, abundance, hierarchy, and taxonomy). These 2D representations enable enhanced disease prediction by established ConvNet-based AggMapNet models, outperforming the commonly used machine learning and deep learning models in metagenomic benchmark datasets. These 2D representations combined with AggMapNet explainable module robustly identified more reliable and replicable disease-prediction microbes (biomarkers). Employing the MEGMA-AggMapNet pipeline for biomarker identification from 5 disease datasets, 84% of the identified biomarkers have been described in over 74 distinct works as important for these diseases. Moreover, the method also discovered highly consistent sets of biomarkers in cross-cohort colorectal cancer (CRC) patients and microbial shifts in different CRC stages.

Toward ordered -omics data science: Researchers on the magic of turning metagenomic chaos into image-like patterns.

Wan Xiang Shen, a postdoctoral researcher at National University of Singapore, and Yu Zong Chen, the PI of the Bioinformatics and Drug Design (BIDD) group, have developed an AI pipeline for enhanced deep learning of metagenomic data. Their Patterns paper highlights the advantages of unsupervised data restructuring in microbiome-based disease prediction and biomarker discovery. They talk about their view of data science and the backstory of the article published in Patterns.

Treatment Outcomes in COVID-19 Patients with Brucellosis: Case Series in Heilongjiang and Systematic Review of Literature / 生物医学与环境科学（英文）

OBJECTIVE@#Clinical characteristics and outcome in COVID-19 with brucellosis patients has not been well demonstrated, we tried to analyze clinical outcome in local and literature COVID-19 cases with brucellosis before and after recovery.@*METHODS@#We retrospectively collected hospitalization data of comorbid patients and prospectively followed up after discharge in Heilongjiang Infectious Disease Hospital from January 15, 2020 to April 29, 2022. Demographics, epidemiological, clinical symptoms, radiological and laboratory data, treatment medicines and outcomes, and follow up were analyzed, and findings of a systematic review were demonstrated.@*RESULTS@#A total of four COVID-19 with brucellosis patients were included. One patient had active brucellosis before covid and 3 patients had nonactive brucellosis before brucellosis. The median age was 54.5 years, and all were males (100.0%). Two cases (50.0%) were moderate, and one was mild and asymptomatic, respectively. Three cases (75.0%) had at least one comorbidity (brucellosis excluded). All 4 patients were found in COVID-19 nucleic acid screening. Case C and D had only headache and fever on admission, respectively. Four cases were treated with Traditional Chinese medicine, western medicines for three cases, no adverse reaction occurred during hospitalization. All patients were cured and discharged. Moreover, one case (25.0%) had still active brucellosis without re-positive COVID-19, and other three cases (75.0%) have no symptoms of discomfort except one case fell fatigue and anxious during the follow-up period after recovery. Conducting the literature review, two similar cases have been reported in two case reports, and were both recovered, whereas, no data of follow up after recovery.@*CONCLUSION@#These cases indicate that COVID-19 patients with brucellosis had favorable outcome before and after recovery. More clinical studies should be conducted to confirm our findings.

In silico approaches uncovering the systematic function of N-phosphorylated proteins in human cells.

Phosphorylation plays a key role in the regulation of protein function. In addition to the extensively studied O-phosphorylation of serine, threonine, and tyrosine, emerging evidence suggests that the non-canonical phosphorylation of histidine, lysine, and arginine termed N-phosphorylation, exists widely in eukaryotes. At present, the study of N-phosphorylation is still in its infancy, and its regulatory role and specific biological functions in mammalian cells are still unknown. Here, we report the in silico analysis of the systematic biological significance of N-phosphorylated proteins in human cells. The protein structural and functional domain enrichment analysis revealed that N-phosphorylated proteins are rich in RNA recognition motif, nucleotide-binding and alpha-beta plait domains. The most commonly enriched biological pathway is the metabolism of RNA. Besides, arginine phosphorylated (pArg) proteins are highly related to DNA repair, while histidine phosphorylated (pHis) proteins may play a role in the regulation of the cell cycle, and lysine phosphorylated (pLys) proteins are linked to cellular stress response, intracellular signal transduction, and intracellular transport, which are of great significance for maintaining cell homeostasis. Protein-protein interaction (PPI) network analysis revealed important hub proteins (i.e., SRSF1, HNRNPA1, HNRNPC, SRSF7, HNRNPH1, SRSF2, SRSF11, HNRNPD, SRRM2 and YBX1) which are closely related to neoplasms, nervous system diseases, and virus infection and have potential as therapeutic targets. Those proteins with clinical significance are worthy of attention, and the rational considerations of N-phosphorylation in occurrence and progression of diseases might be beneficial for further translational applications.