Risk factors and predicting nomogram for the clinical deterioration of non-severe community-acquired pneumonia.

BACKGROUND: Currently, there remains insufficient focus on non-severe community-acquired pneumonia (CAP) patients who are at risk of clinical deterioration, and there is also a dearth of research on the related risk factors. Early recognition of hospitalized patients at risk of clinical deterioration will be beneficial for their clinical management. METHOD: A retrospective study was conducted in The First Affiliated Hospital of Wenzhou Medical University, China, spanning from January 1, 2018 to April 30, 2022, and involving a total of 1,632 non-severe CAP patients. Based on whether their condition worsened within 72 h of admission, patients were divided into a clinical deterioration group and a non-clinical deterioration group. Additionally, all patients were randomly assigned to a training set containing 75% of patients and a validation set containing 25% of patients. In the training set, risk factors for clinical deterioration in patients with non-severe CAP were identified by using LASSO regression analysis and multivariate logistic regression analysis. A nomogram was developed based on identified risk factors. The effectiveness of the nomogram in both the training and validation sets was assessed using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: Age, body mass index (BMI), body temperature, cardiovascular comorbidity, respiratory rate, LDH level, lymphocyte count and D-dimer level were identified as risk factors associated with the clinical deterioration of non-severe CAP within 72 h of admission. The area under curve (AUC) value of the nomogram was 0.78 (95% CI: 0.74-0.82) in the training set and 0.75 (95% CI: 0.67-0.83) in the validation set. Furthermore, the calibration curves for both the training and validation sets indicated that the predicted probability of clinical deterioration aligned with the actual probability. Additionally, DCA revealed clinical utility for the nomogram at a specific threshold probability. CONCLUSION: The study successfully identified the risk factors linked to the clinical deterioration of non-severe CAP and constructed a nomogram for predicting the probability of deterioration. The nomogram demonstrated favorable predictive performance and has the potential to aid in the early identification and management of non-severe CAP patients at elevated risk of deterioration.

Effectiveness of opportunistic osteoporosis screening on chest CT using the DCNN model.

OBJECTIVE: To develop and evaluate a deep learning model based on chest CT that achieves favorable performance on opportunistic osteoporosis screening using the lumbar 1 + lumbar 2 vertebral bodies fusion feature images, and explore the feasibility and effectiveness of the model based on the lumbar 1 vertebral body alone. MATERIALS AND METHODS: The chest CT images of 1048 health check subjects from January 2021 to June were retrospectively collected as the internal dataset (the segmentation model: 548 for training, 100 for tuning and 400 for test. The classification model: 530 for training, 100 for validation and 418 for test set). The subjects were divided into three categories according to the quantitative CT measurements, namely, normal, osteopenia and osteoporosis. First, a deep learning-based segmentation model was constructed, and the dice similarity coefficient(DSC) was used to compare the consistency between the model and manual labelling. Then, two classification models were established, namely, (i) model 1 (fusion feature construction of lumbar vertebral bodies 1 and 2) and (ii) model 2 (feature construction of lumbar 1 alone). Receiver operating characteristic curves were used to evaluate the diagnostic efficacy of the models, and the Delong test was used to compare the areas under the curve. RESULTS: When the number of images in the training set was 300, the DSC value was 0.951 ± 0.030 in the test set. The results showed that the model 1 diagnosing normal, osteopenia and osteoporosis achieved an AUC of 0.990, 0.952 and 0.980; the model 2 diagnosing normal, osteopenia and osteoporosis achieved an AUC of 0.983, 0.940 and 0.978. The Delong test showed that there was no significant difference in area under the curve (AUC) values between the osteopenia group and osteoporosis group (P = 0.210, 0.546), while the AUC value of normal model 2 was higher than that of model 1 (0.990 vs. 0.983, P = 0.033). CONCLUSION: This study proposed a chest CT deep learning model that achieves favorable performance on opportunistic osteoporosis screening using the lumbar 1 + lumbar 2 vertebral bodies fusion feature images. We further constructed the comparable model based on the lumbar 1 vertebra alone which can shorten the scan length, reduce the radiation dose received by patients, and reduce the training cost of technologists.

A new resorcylic acid lactone from the endophytic fungus Chaetosphaeronema sp.

A new 14-membered resorcylic acid lactone (RAL14), chaetolactone A (1), along with three known ones (2-4), was obtained from the fermentation of the soil-derived fungus Chaetosphaeronema sp. SSJZ001. Their structures were established based on extensive spectroscopic data analyses (UV, IR, HRESIMS, 1D, and 2D NMR),13C NMR chemical shifts calculations coupled with the DP4+ probability method, theoretical calculations of ECD spectra, as well as X-ray diffraction analysis. All compounds were evaluated for their cytotoxic effects against A549, HO-8910, and MCF-7 cell lines.

Two unusual novel iridoid glycosides from Cornus officinalis fruit and their biological activities.

Two unusual novel iridoid glycosides, cornsecoside A (1) and cornsecoside B (2), were isolated from a 40% ethanol elution fraction of a 50% ethanol extract of Cornus officinalis fruit. Their structures were determined by spectroscopic data analysis combined with hydrolysis and ECD spectroscopy. In addition, compounds 1 and 2 exhibited cytotoxic activity against Bel-7402 cells with IC50 values of 8.12 and 9.31 µM, and were neuroprotective against H2O2-induced SH-SY5Y cell injure at a concentration of 10 µM.

Diagnostic performance of the metagenomic next-generation sequencing in lung biopsy tissues in patients suspected of having a local pulmonary infection.

PURPOSE: This study aims to evaluate the diagnostic application and performance of the metagenomic next-generation sequencing (mNGS) in patients suspected of local pulmonary infection by comparing it to the traditional pathogen detection methods in lung tissue specimens obtained by a computerized tomography-guided biopsy (CT-guided biopsy). METHODS: We retrospectively reviewed patients, admitted to the First Affiliated Hospital of Wenzhou Medical University, China from May 2018 to December 2020, who were suspected of local pulmonary infection. All cases received a CT-guided lung biopsy, tissue samples were sent both for conventional examinations (CE) and mNGS tests. The sensitivity and specificity of the two diagnostic approaches were compared. RESULTS: 106 patients enrolled, 76 patients were diagnosed with a pulmonary infection. Among 49 patients with identified pathogens, CE confirmed pathogenic infections in 32 cases. Mycobacterium spp. and fungi accounted for 37.5% (12/32) and 28.1% (9/32), respectively, with bacteria 34.4% (11/32). The mNGS examination detected extra pathogenic microorganisms in 22 patients that were consistent with the patients' clinical and radiographic pictures. The sensitivity of mNGS was 53.9% vs. 42.1% for the CE, while the specificity was 56.7% versus 96.7%. For detection rate, mNGS was significantly superior to CE in bacterial (96.3% vs. 40.7%, p < 0.05), and mixed infections (100% vs. 50%, p < 0.05), but inferior to CE in fungal (60% vs. 90%, p > 0.05) and Mycobacterium spp. infections (66.7% vs. 100%, p > 0.05) with no significant difference. Among 31 cases diagnosed with lung abscess, the diagnostic performance of the detection rate was 67.7% (21/31) in favour of mNGS compared to 29.0% (9/31) for CE (p < 0.05). Most polymicrobial infections were induced by anaerobic species that coexisted with Streptococcus constellatus. And Klebsiella pneumoniae was the most common isolated monomicrobial infection. CONCLUSIONS: The most commonly detected causative pathogens for local pulmonary infections were bacteria, Mycobacterium spp. and fungi. Compared with the CE, the advantages of mNGS in the pathogens detection lie in the discovery of bacterial and mixed infections, as well as in the detection of lung abscess. Conversely, mNGS is not good enough to be recommendable for the detection of Mycobacterium spp. and fungi.

Two New Nor-seco-Isodhilarane-Type Meroterpenoids from the Endophytic Fungus Penicillium purpurogenum.

Two new nor-seco isodhilarane meroterpenoids (NSIMs), purpurogenolides F (1) and G (2), along with three known meroterpenoid analogs (3-5), were isolated from the cultures of an endophytic fungus, Penicillium purpurogenum. Structures and absolute configurations of the new NSIMs were determined based on extensive spectroscopic data analyses, including HR-ESI-MS, UV, IR, NMR chemical shift calculations together with DP4+ probability analysis, as well as ECD calculations. All the isolated meroterpenoids were assessed for their anti-inflammatory activities, and compound 4 exhibited moderate inhibitory activity against the nitric oxide (NO) production in lipopolysaccharide (LPS) induced RAW 264.7 cells with an IC50 value of 20.85±2.31 µM.

Chemical characterization of a PD-1/PD-L1 inhibitory activity fraction of the ethanol extract from Gymnadenia conopsea.

Searching for PD-1/PD-L1 inhibitor from medicinal plants has become a potential method to discover small molecular cancer immunotherapy drugs. Using PD-1/PD-L1 inhibitory activity assay in vitro, a bioactive fraction was obtained from the ethanol extract of Gymnadenia conopsea. A sensitive UPLC-HRMS/MS method was established for the rapid screening and identification of compositions from bioactive fraction. Based on the characteristic fragmentation patterns of standards analysis and extracted ion chromatogram (EIC) method, 46 compounds were rapidly screened and identified (including 35 succinic acid ester glycosides and 11 other compounds), among which 17 compounds were tentatively identified as new compounds.

[Research progress on chemical constituents and pharmacological activities of Trigonella foenum-graecum].

Trigonella foenum-graecum is an annual plant of the genus Trigonella in the Leguminosae family. It is widely distributed in China and has a long history of application. According to phytochemistry research, the seeds, stem, and leaves of this herb contain not only a variety of bioactive ingredients, including alkaloids, saponins, polysaccharides, flavonoids, and phenols, but also abundant nutrients such as unsaturated fatty acids and amino acids and various trace elements. Pharmacological studies have shown that both the extract of T. foenum-graecum and its chemical constituents exhibit hypoglycemic, hypolipidemic, antitumor, antioxidative, antimicro-bial, and hepatoprotective activities. This paper reviews the research progress on the chemical constituents and pharmacological effects of T. foenum-graecum, which may contribute to further development, application, and clinical research of this herb.

[Construction and application of pharmacophore model of human carboxylesterase 2 inhibitors].

According to human carboxylesterase 2(hCE2) inhibitors reported in the literature, the pharmacophore model of hCE2 inhibitors was developed using HipHop module in Discovery Studio 2016. The optimized pharmacophore model, which was validated by test set, contained two hydrophobic, one hydrogen bond acceptor, and one aromatic ring features. Using the pharmacophore model established, 5 potential hCE2 inhibitors(CS-1,CS-2,CS-3,CS-6 and CS-8) were screened from 20 compounds isolated from the roots of Paeonia lactiflora, which were further confirmed in vitro, with the IC_(50) values of 5.04, 5.21, 5.95, 6.64 and 7.94 µmol·L~(-1), respectively. The results demonstrated that the pharmacophore model exerted excellent forecasting ability with high precision, which could be applied to screen novel hCE2 inhibitors from Chinese medicinal materials.

Three new polyketides from the endophytic fungus Penicillium purpurogenum.

Three new polyketides including two benzophenone derivatives, penibenzones A and B (1 and 2), and a new phthalide derivative, penibenzone C (3), along with six known compounds, were isolated from the solid-substrate cultures of the endophytic fungus Penicillium purpurogenum IMM003. Their structures were elucidated on the basis of spectroscopic data (UV, IR, HRESIMS, 1D and 2D NMR). All compounds were evaluated for inhibitory activity against pancreatic lipase, but none of them displayed activity.[Formula: see text].

Rapid Characterizaiton of Chemical Constituents of the Tubers of Gymnadenia conopsea by UPLC-Orbitrap-MS/MS Analysis.

Gymnadenia conopsea R. Br. is a traditional Tibetan medicinal plant that grows at altitudes above 3000 m, which is used to treat neurasthenia, asthma, coughs, and chronic hepatitis. However, a comprehensive configuration of the chemical profile of this plant has not been reported because of the complexity of its chemical constituents. In this study, a rapid and precise method based on ultra-high performance liquid chromatography (UPLC) combined with an Orbitrap mass spectrometer (UPLC-Orbitrap-MS/MS) was established in both positive- and negative-ion modes to rapidly identify various chemical components in the tubers of G. conopsea for the first time. Finally, a total of 91 compounds, including 17 succinic acid ester glycosides, 9 stilbenes, 6 phenanthrenes, 19 alkaloids, 11 terpenoids and steroids, 20 phenolic acid derivatives, and 9 others, were identified in the tubers of G. conopsea based on the accurate mass within 3 ppm error. Furthermore, many alkaloids, phenolic acid derivates, and terpenes were reported from G. conopsea for the first time. This rapid method provides an important scientific basis for further study on the cultivation, clinical application, and functional food of G. conopsea.

[Regional suitability analysis of endangered medicinal plant Dactylorhiza hatagirea].

The wild plant Dactylorhiza hatagirea commonly used in Tibetan medicine. In recent years, it is on the verge of extinction due to improper excavation and habitat deterioration. Based on the literature review, and the meteorological and ecological data of Qinghai Province and data derived from the field survey, this study took the altitude, humidity index, annual average wind speed, monthly average sunshine, precipitation and temperature in growing season, soil type and vegetation type as the zoning indexes, and applied ArcGIS software for spatial superposition analysis. The results showed that the D. hatagirea was primarily found in the alpine meadow in the east and south of Qinghai Province. The overall suitable area was about 30 700 km~2. Taking Qinghai Lake as the boundary for reference, the area in the south appeared to be more suitable, while the area in the east appeared to be secondary in suitability. Henan County, Zeku County, Jiuzhi County and Banma County were the main suitable areas covering the expanse of 12 454 km~2. Through the field verification, such zoning results appeared to be accurate, precise and illustrative. The spatial distribution of ecological suitability provides the decision-making basis for the resource investigation, environment protection and comprehensive resource management. It also provides the empirical case for developing the interdisciplinary research method in the suitability study of medicinal plants.

[Extraction technology, composition analysis and antioxidant and antimicrobial activities of volatile oil from fenugreek leaves].

To define the extraction process, main components and antioxidative and antimicrobial activities of volatile oil from fenugreek(Trigonella foenum-graecum) leaves and its active substance basis. Response surface methodology was used for optimum supercritical CO_2 extraction conditions of essential oil from fenugreek leaves. The main components of volatile oil were analyzed by GC-MS, its antioxidant activity was evaluated by measuring the scavenging ability of 1,1-diphenyl-2-picrylhydrazyl(DPPH) and 2, 2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid, ABTS) free radical, and the antimicrobial effect of volatile oil was evaluated by K-B paper AGAR diffusion method. The results showed that the optimal extraction temperature was 50 ℃, the extraction time was 89 min, and the extraction pressure was 35 MPa. Under the conditions, the optimum extracting yield of volatile oil was 1.72%,which was about 1.5 times higher than that of the conventional steam distillation. A total of 52 compounds were found based on reference substance retention time and GC-MS fragmentation information or the existing literatures, and the major compounds were oleic acid(9.65%), carveol(9.41%), n-hexadecanoic acid(9.1%), linoleic acid(6.95%), methyl linolenate(5.4%), petroselinic acid(5.3%), testosterone(3.4%), sotolon(1.75%). The volatile oil of fenugreek showed moderate antioxidant activities in DPPH assay(IC_(50) value of 0.473 mg·mL~(-1)) and ABTS test(IC_(50) value of 0.107 mg·mL~(-1)). The oil had a stronger antimicrobial activity in vitro. MIC of the volatile oil ranged from 0.375 to 1.5 mg·mL~(-1). The results showed that the optimized volatile oil extraction process was stable, and the extraction yield was high. Fenugreek leaves contained a variety of volatile components, with obvious antioxidant and antibacterial activities. This study provides a certain theoretical basis for the comprehensive development and utilization of fenugreek.

Cell-Wide Survey of Amide-Bonded Lysine Modifications by Using Deacetylase CobB.

BACKGROUND: Lysine post-translational modifications are important regulators of protein function. Proteomic and biochemical approaches have resulted in identification of several lysine modifications, including acetylation, crotonylation, and succinylation. Here, we developed an approach for surveying amide-bonded lysine modifications in the proteome of human tissues/cells based on the observation that many lysine modifications are amide-bonded and that the Salmonella enterica deacetylase, CobB, is an amidase. RESULTS: After the proteome of human tissues/cells was denatured and the non-covalently bonded metabolites were removed by acetone washes, and the amide-bonded modifiers were released by CobB and analyzed using liquid- and/or gas chromatography/mass spectrometry metabolomic analysis. This protocol, which required 3-4 days for completion, was used to qualitatively identify more than 40 documented and unreported lysine modifications from the human proteome and to quantitatively analyze dynamic changes in targeted amide-bonded lysine modifications. CONCLUSIONS: We developed a method that was capable of monitoring and quantifying amide-bonded lysine modifications in cells of different origins.

[Study on sustainable development of Tibetan medicine resources on Qinghai-Tibet Plateau].

Through using the theoretical research results of traditional Chinese medicine resource management and sustainable utilization as references,this article systematically analyzes the relationship between economic subsystems for utilization of wild renewable resources in Tibetan medicine,resources,environment and social systems. It sorts out and designs the relationship diagram of the sustainable utilization system for Tibetan medicine resources,input and output relationship diagrams of economics subsystems for Tibetan medicine resources,development model diagram for Tibetan medical industrial chain,market development flow chart of wild herb resources of Tibetan medicine,causal circuit diagram of sustainable use mechanism for Tibetan medicine resources,cause tree of the stock and function for Tibetan medicine resources,cause tree of total income for Tibetan medicine resource utilization and tree diagram of compensation fund usage of Tibetan medicine resources. It provides an innovative research method for Tibetan medicine resources,and theoretical basis and relevant countermeasure for constructing sustainable approaches to development of wild renewable resources in Tibetan medicine.

[Limonoids from seeds of Azadirachta indica and their antibacterial activity].

Fifteen limonoids were isolated from 95% ethanol extracts of the dry seeds of neem( Azadirachta indica) by various column chromatography techniques including silica gel,Pharmadex LH-20 gel and ODS resin. Based on spectroscopic analysis,their structures were determined as nimbocinol( 1),17ß-hydroxynimbocinol( 2),1α,3α,7α-triacetylvilasinin( 3),7α-benzoyltrichilinin( 4),1,3-diacetyl-7-tigloyl-12-hydroxyvilasinin( 5),3-deacetylsalannin( 6),1-O-acetyl-1-detigloylsalannin( 7),2'( R),3'-dihydrosalannin( 8),2'( S),3'-dihydrosalannin( 9),2,3-dihydronimbolide( 10),6-homodesacetylnimbin( 11),gedunin( 12),7-deacetyl-7-epi-dihydrogedunin( 13),7-deacetoxy-7α-hydroxygedunin( 14) and nimbinene( 15). Compound 7 is a new natural product. 4,8,9,13 and 14 are isolated from the genus Azadirachta for the first time. Compound 2 showed inhibitory activity against Escherichia coli and Staphylococcus epidermidis,with MIC values of 32 and 128 mg·L~(-1),respectively. Compound 10 showed moderate inhibitory activity against S. epidermidis with a MIC value of 64 mg·L~(-1). Compound 11 inhibited the growth of E. coli and Pseudomonas aeruginosa,both with MIC values of 128 mg·L~(-1). Compound 15 exhibited inhibitory activity against P. aeruginosa,with a MIC value of128 mg·L~(-1).

A new cytotoxic 12-membered macrolactone from the endophytic fungus Exserohilum rostratum LPC-001.

A new oxacyclododecindione-type macrolactone, (13R,14S,15R)-13-hydroxy-14-deoxyoxacyclododecindione (1), has been obtained from the solid cultures of the fungus Exserohilum rostratum, a fungal strain endophytic in Gymnadenia conopsea. Its structure, including the absolute configuration, was extensively established by 1D and 2D NMR data, the modified Mosher method, and a combination of experimental and theoretically calculated electronic circular dichroism (ECD) spectra. Compound 1 showed weak selective cytotoxicity against the A549 lung cell line with an IC50 value of 9.2 µM.

Two new terpenoid ester glycosides from the twigs of Litsea cubeba.

A new sesquiterpenoid ester glycoside (1) and a new monoterpenoid ester glycoside (2) have been isolated from an ethanol extract of the twigs of Litsea cubeba. Their structures were elucidated by extensive 1D- and 2D-NMR experiments, and the absolute configurations were determined by chemical methods, specific rotation, and a combination of experimental and theoretically calculated electronic circular dichroism spectra. Compound 1 exhibited selective cytotoxicity against A549 and HCT-8 cell lines with the IC50 values of 8.9 and 9.6 µM, respectively.

[Study on structural conversion of dihydrochelerythrine in different solvents].

Dihydrochelerythrine was isolated from the ethanol extract of Corydalis yanhusuo by chromatographic and recrystallization techniques. To our knowledge, this is the first report that dihydrochelerythrine was found to be unstable. The NMR, HPLC, and LC-MS were applied to monitor the structural conversion process of dihydrochelerythrine. The results showed that when dissolved in polar deuteration solvent (e.g., DMSO-d6 & MeOD), dihydrochelerythrine is directly converted to chelerythrine gradually. However, if used non-polar reagent (e.g.,CD2Cl2), the sample of dihydrochelerythrine undergoes the formation of pseudobase, chelerythrine, and bimolecular ether then followed by oxidation to oxychelerythrine as the major final product. Which leads to this phenomenon maybe is that the C-6 in dihydrochelerythrine is highly reactive to nucleophiles, and is easily converted to different derivatives in different solvents attributed to the solvent effect. This finding will contribute to the extraction and isolation, bioactivity screening, and quality evaluation of medicinal materials containing dihydrochelerythrine and other similar derivatives.

DeepmdQCT: A multitask network with domain invariant features and comprehensive attention mechanism for quantitative computer tomography diagnosis of osteoporosis.

In the medical field, the application of machine learning technology in the automatic diagnosis and monitoring of osteoporosis often faces challenges related to domain adaptation in drug therapy research. The existing neural networks used for the diagnosis of osteoporosis may experience a decrease in model performance when applied to new data domains due to changes in radiation dose and equipment. To address this issue, in this study, we propose a new method for multi domain diagnostic and quantitative computed tomography (QCT) images, called DeepmdQCT. This method adopts a domain invariant feature strategy and integrates a comprehensive attention mechanism to guide the fusion of global and local features, effectively improving the diagnostic performance of multi domain CT images. We conducted experimental evaluations on a self-created OQCT dataset, and the results showed that for dose domain images, the average accuracy reached 91%, while for device domain images, the accuracy reached 90.5%. our method successfully estimated bone density values, with a fit of 0.95 to the gold standard. Our method not only achieved high accuracy in CT images in the dose and equipment fields, but also successfully estimated key bone density values, which is crucial for evaluating the effectiveness of osteoporosis drug treatment. In addition, we validated the effectiveness of our architecture in feature extraction using three publicly available datasets. We also encourage the application of the DeepmdQCT method to a wider range of medical image analysis fields to improve the performance of multi-domain images.