Sporolactobacillus pectinivorans sp. nov., an anaerobic bacterium isolated from spoiled jelly.

A Gram-positive-staining, endospore-forming, facultatively anaerobic, lactic-acid-producing bacterium, strain GD201205T, was isolated from spoiled jelly in China. Strain GD201205T fermented glucose, fructose, mannose, sucrose, raffinose and turanose, but negative for nitrate reduction, catalase and oxidase. The predominant fatty acids of the strain were anteiso-C17 : 0 and anteiso-C15 : 0. Whole-cell hydrolysates contained glycine and alanine with meso-iaminopimelic acid as the diagnostic diamino acid. The major polar lipids were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylcholine, glycolipid 1 and glycolipid 2. The DNA G+C content of strain GD201205T was 48.7 mol%. 16S rRNA gene sequence analysis indicated that the strain belonged to the genus Sporolactobacillus and was most closely related to Sporolactobacillus vineaeKCTC 5376T and Sporolactobacillus putidusJCM 15325T with 16S rRNA gene sequence similarities of 97.5 and 96.9 %, respectively. Levels of DNA-DNA relatedness between strain GD201205T and Sporolactobacillus vineaeKCTC 5376Tand Sporolactobacillus putidusJCM 15325T were 29.2 and 47.6 %, respectively. Phylogenetic analysis based on the 16S rRNA gene and gyrB gene revealed that strain GD201205T was clearly distinct from all related species of the genus Sporolactobacillus. On the basis of the phylogenetic, chemotaxonomic and phenotypic evidence given in this study, strain GD201205T should be classified as a representative of a novel species of the genus Sporolactobacillus for which the name Sporolactobacillus pectinivorans is proposed. The type strain is GD201205T (=CICC 23867T=KCTC 15488T).

Nickel-Catalyzed Decarboxylative Difluoroalkylation of α,ß-Unsaturated Carboxylic Acids.

The first example of nickel-catalyzed decarboxylative fluoroalkylation of α,ß-unsaturated carboxylic acids has been developed with commonly available fluoroalkyl halides. This novel transformation has demonstrated broad substrate scope, excellent functional-group tolerance, mild reaction conditions, and excellent stereoselectivity. Mechanistic investigations indicate that a fluoroalkyl radical is involved in the catalytic cycle.

Pd(II)-catalyzed C-H arylation of aryl and benzyl Weinreb amides.

The first example of palladium-catalyzed ortho-C-H arylation of aryl and benzyl Weinreb amides was developed, in which HOTf was used as a key promoter. This method exhibits good functional group tolerance, a broad substrate scope of both Weinreb amides and aryl iodides, high mono-selectivity and mild reaction conditions.

Pd(II)-catalyzed, controllable C-H mono-/diarylation of aryl tetrazoles: concise synthesis of Losartan.

A palladium(II)-catalyzed C-H arylation directed by tetrazole, a metabolically stable surrogate for the carboxylic acid group in drug design, has been developed. Excellent mono-/di-selectivity was achieved through adjustment of the protecting site on the tetrazole ring. The synthetic utility of this new transformation was demonstrated in the concise total synthesis of Losartan.

Nickel-catalyzed monofluoromethylation of aryl boronic acids.

Aryl boronic acids can be monofluoromethylated under nickel catalysis. The utility of this method is demonstrated by the monofluoromethylation of a borylated and acyl-protected derivative of the statin drug ezetimibe. Mechanistic investigations indicate that a fluoromethyl radical is involved in the Ni(I)/Ni(III) catalytic cycle.

Exploring simple triplet representation learning.

Fully supervised learning methods necessitate a substantial volume of labelled training instances, a process that is typically both labour-intensive and costly. In the realm of medical image analysis, this issue is further amplified, as annotated medical images are considerably more scarce than their unlabelled counterparts. Consequently, leveraging unlabelled images to extract meaningful underlying knowledge presents a formidable challenge in medical image analysis. This paper introduces a simple triple-view unsupervised representation learning model (SimTrip) combined with a triple-view architecture and loss function, aiming to learn meaningful inherent knowledge efficiently from unlabelled data with small batch size. With the meaningful representation extracted from unlabelled data, our model demonstrates exemplary performance across two medical image datasets. It achieves this using only partial labels and outperforms other state-of-the-art methods. The method we present herein offers a novel paradigm for unsupervised representation learning, establishing a baseline that is poised to inspire the development of more intricate SimTrip-based methods across a spectrum of computer vision applications. Code and user guide are released at https://github.com/JerryRollingUp/SimTripSystem, the system also runs at http://43.131.9.159:5000/.

Development of a machine learning-based model to predict major adverse events after surgery for type A aortic dissection complicated by malnutrition.

Objective: This study aims to develop a predictive model for the risk of major adverse events (MAEs) in type A aortic dissection (AAAD) patients with malnutrition after surgery, utilizing machine learning (ML) algorithms. Methods: We retrospectively collected clinical data from AAAD patients with malnutrition who underwent surgical treatment at our center. Through least absolute shrinkage and selection operator (LASSO) regression analysis, we screened for preoperative and intraoperative characteristic variables. Based on the random forest (RF) algorithm, we constructed a ML predictive model, and further evaluated and interpreted this model. Results: Through LASSO regression analysis and univariate analysis, we ultimately selected seven feature variables for modeling. After comparing six different ML models, we confirmed that the RF model demonstrated the best predictive performance in this dataset. Subsequently, we constructed a model using the RF algorithm to predict the risk of postoperative MAEs in AAAD patients with malnutrition. The test set results indicated that this model has excellent predictive efficacy and clinical applicability. Finally, we employed the Shapley additive explanations (SHAP) method to further interpret the predictions of this model. Conclusion: We have successfully constructed a risk prediction model for postoperative MAEs in AAAD patients with malnutrition using the RF algorithm, and we have interpreted the model through the SHAP method. This model aids clinicians in early identification of high-risk patients for MAEs, thereby potentially mitigating adverse clinical outcomes associated with malnutrition.

Cancer screening in hospitalized ischemic stroke patients: a multicenter study focused on multiparametric analysis to improve management of occult cancers.

Background/aims: The reciprocal promotion of cancer and stroke occurs due to changes in shared risk factors, such as metabolic pathways and molecular targets, creating a "vicious cycle." Cancer plays a direct or indirect role in the pathogenesis of ischemic stroke (IS), along with the reactive medical approach used in the treatment and clinical management of IS patients, resulting in clinical challenges associated with occult cancer in these patients. The lack of reliable and simple tools hinders the effectiveness of the predictive, preventive, and personalized medicine (PPPM/3PM) approach. Therefore, we conducted a multicenter study that focused on multiparametric analysis to facilitate early diagnosis of occult cancer and personalized treatment for stroke associated with cancer. Methods: Admission routine clinical examination indicators of IS patients were retrospectively collated from the electronic medical records. The training dataset comprised 136 IS patients with concurrent cancer, matched at a 1:1 ratio with a control group. The risk of occult cancer in IS patients was assessed through logistic regression and five alternative machine-learning models. Subsequently, select the model with the highest predictive efficacy to create a nomogram, which is a quantitative tool for predicting diagnosis in clinical practice. Internal validation employed a ten-fold cross-validation, while external validation involved 239 IS patients from six centers. Validation encompassed receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and comparison with models from prior research. Results: The ultimate prediction model was based on logistic regression and incorporated the following variables: regions of ischemic lesions, multiple vascular territories, hypertension, D-dimer, fibrinogen (FIB), and hemoglobin (Hb). The area under the ROC curve (AUC) for the nomogram was 0.871 in the training dataset and 0.834 in the external test dataset. Both calibration curves and DCA underscored the nomogram's strong performance. Conclusions: The nomogram enables early occult cancer diagnosis in hospitalized IS patients and helps to accurately identify the cause of IS, while the promotion of IS stratification makes personalized treatment feasible. The online nomogram based on routine clinical examination indicators of IS patients offered a cost-effective platform for secondary care in the framework of PPPM. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00354-8.

Residual Block Based Nested U-Type Architecture for Multi-Modal Brain Tumor Image Segmentation.

Multi-modal magnetic resonance imaging (MRI) segmentation of brain tumors is a hot topic in brain tumor processing research in recent years, which can make full use of the feature information of different modalities in MRI images, so that tumors can be segmented more effectively. In this article, convolutional neural networks (CNN) is used as a tool to improve the efficiency and effectiveness of segmentation. Based on this, Dense-ResUNet, a multi-modal MRI image segmentation model for brain tumors is created. The Dense-ResUNet consists of a series of nested dense convolutional blocks and a U-Net shaped model with residual connections. The nested dense convolutional blocks can bridge the semantic disparity between the feature maps of the encoder and decoder before fusion and make full use of different levels of features. The residual blocks and skip connection can extract pixel information from the image and skip the link to solve the traditional deep traditional CNN network problem. The experiment results show that our Dense-ResUNet can effectively help to extract the brain tumor and has great clinical research and application value.

[In vivo reflectance spectroscopy study of different clearing agents on human skin optical clearing].

The changes of skin tissue reflectance spectroscopy before and after being treated with the optical clearing agents of three different types of optical clearing within the wavelength rang of 400-1 000 nm, and the degree of changes in reflectance spectroscopy of each group skin during 0-60 min at 580 nm in vivo were real-time dynamically researched. The reflectance spectroscopy of skin tissue before and after being dealt by the optical clearing agents of glycerol, glucose and propylene glycol was measured using a USB-4000 fiber spectrophotometer at 0, 10, 20, 30, 40, 50 and 60 min. The results showed that the reflectance spectral intensity was distinctly decreased, but the reflectance was significantly increased gradually with the time prolonged. However, different optical clearing agents have different clearing progress. The relative decrease of reflectance of palm skin tissue before and after being dealt by the optical agents of 40% glycerol, 40% glucose and 40% propylene glycol during 10, 20, 30, 40, 50 and 60 min at the wavelength 580 nm is 5%, 7%, 9%, 10%, 11% and 12%, 9%, 13%, 16%, 19%, 21% and 22%, and 14%, 22%, 29%, 32%, 34% and 35%, respectively. The significant improvement in light transmittance and enhancement of light penetration through tissue was demonstrated for all solutions. The effect and processes of optical clearing of skin tissue is not only closely related to the choosing of the clearing agent type, but also related to the treatment time with the skin tissue. The clearing progress of different type optical clearing agent showed the order of 40% propylene glycol, 40% glucose and 40% glycerol.

Selective Cross-Subject Transfer Learning Based on Riemannian Tangent Space for Motor Imagery Brain-Computer Interface.

A motor imagery (MI) brain-computer interface (BCI) plays an important role in the neurological rehabilitation training for stroke patients. Electroencephalogram (EEG)-based MI BCI has high temporal resolution, which is convenient for real-time BCI control. Therefore, we focus on EEG-based MI BCI in this paper. The identification of MI EEG signals is always quite challenging. Due to high inter-session/subject variability, each subject should spend long and tedious calibration time in collecting amounts of labeled samples for a subject-specific model. To cope with this problem, we present a supervised selective cross-subject transfer learning (sSCSTL) approach which simultaneously makes use of the labeled samples from target and source subjects based on Riemannian tangent space. Since the covariance matrices representing the multi-channel EEG signals belong to the smooth Riemannian manifold, we perform the Riemannian alignment to make the covariance matrices from different subjects close to each other. Then, all aligned covariance matrices are converted into the Riemannian tangent space features to train a classifier in the Euclidean space. To investigate the role of unlabeled samples, we further propose semi-supervised and unsupervised versions which utilize the total samples and unlabeled samples from target subject, respectively. Sequential forward floating search (SFFS) method is executed for source selection. All our proposed algorithms transfer the labeled samples from most suitable source subjects into the feature space of target subject. Experimental results on two publicly available MI datasets demonstrated that our algorithms outperformed several state-of-the-art algorithms using small number of the labeled samples from target subject, especially for good target subjects.

[Gastric cancer detection using kubelka-Munk spectral function of DNA and protein absorption bands].

Differential diagnosis for epithelial tissues of normal human gastric, undifferentiation gastric adenocarcinoma, gastric squamous cell carcinomas, and poorly differentiated gastric adenocarcinoma were studied using the Kubelka-Munk spectral function of the DNA and protein absorption bands at 260 and 280 nm in vitro. Diffuse reflectance spectra of tissue were measured using a spectrophotometer with an integrating sphere attachment. The results of measurement showed that for the spectral range from 250 to 650 nm, pathological changes of gastric epithelial tissues induced that there were significant differences in the averaged value of the Kubelka-Munk function f(r infinity) and logarithmic Kubelka-Munk function log[f(r infinity)] of the DNA absorption bands at 260 nm between epithelial tissues of normal human stomach and human undifferentiation gastric cancer, between epithelial tissues of normal human stomach and human gastric squamous cell carcinomas, and between epithelial tissues of normal human stomach and human poorly differentiated cancer. Their differences were 68.5% (p < 0.05), 146.5% (p < 0.05), 282.4% (p < 0.05), 32.4% (p < 0.05), 56.00 (p < 0.05) and 83.0% (p < 0.05) respectively. And pathological changes of gastric epithelial tissues induced that there were significant differences in the averaged value of the Kubelka-Munk function f(r infinity) and logarithmic Kubelka-Munk function log[f(r infinity)] of the protein absorption bands at 280 nm between epithelial tissues of normal human stomach and human undifferentiation gastric cancer, between epithelial tissues of normal human stomach and human gastric squamous cell carcinomas, and between epithelial tissues of normal human stomach and human poorly differentiated cancer. Their differences were 86.8% (p < 0.05), 262.9% (p < 0.05), 660.1% (p < 0.05) and 34% (p < 0.05), 72. 2% (p < 0.05), 113.5% (p < 0.05) respectively. And pathological changes of gastric epithelial tissues induced that there were significant differences in the averaged value of the Kubelka-Munk function f(r infinity) and logarithmic Kubelka-Munk function log[f(r infinity)] of the carotene absorption bands at 480 nm between epithelial tissues of normal human stomach and human undifferentiation gastric cancer, between epithelial tissues of normal human stomach and human gastric squamous cell carcinomas, and between epithelial tissues of normal human stomach and human poorly differentiated cancer. Their differences were 59.5% (p < 0.05), 73% (p < 0.05), 258.9% (p < 0.05), 118.7% (p < 0.05), 139.2% (p < 0.05), and 324. 6% (p < 0.05) respectively. It is obvious that pathological changes of gastric epithelial tissues induced that there were significant changes in the contents of the DNA, protein and beta-carotene of gastric epithelial tissues. The conclusion can be applied to rapid, low-cost and noninvasive the optical biopsy for gastric cancer and provides a useful reference.

Enantioselective Copper-Catalyzed 1,5-Cyanotrifluoromethylation of Vinylcyclopropanes.

A copper-catalyzed enantioselective 1,5-cyanotrifluoromethylation of vinylcyclopropanes has been developed using a radical relay strategy. This asymmetric reaction has demonstrated high enantioselective control, broad substrate scope, and mild conditions. Initiated by the in situ generated CF3 radical from Togni's reagent, this method offers a new solution for remote enantioselective bifunctionalization of alkenes and thus provides a straightforward way for the synthesis of chiral CF3-containing internal alkenylnitriles.

Enantioselective Copper-Catalyzed Cyanation of Remote C(sp3)-H Bonds Enabled by 1,5-Hydrogen Atom Transfer.

The direct functionalization of C(sp3)-H bonds has led to the development of methods to access molecules or intermediates from basic chemicals in an atom- and step-economic fashion. Nevertheless, achieving high levels of chemo-, regio-, and enantioselectivity in these reactions remains challenging due to the ubiquity and low reactivity of C(sp3)-H bonds. Herein, we report an unprecedented protocol for enantioselective cyanation of remote C(sp3)-H bonds. With chiral Box-Cu complex as the catalyst, the reaction of N-fluorosulfonamide furnishes the corresponding products in excellent yields and high enantiomeric excess (ee) under mild reaction conditions. A radical relay pathway involving 1,5-hydrogen atom transfer (1,5-HAT) of N-center radicals followed by enantioselective cyanation of the in situ-formed benzyl radicals is proposed. This enantioselective copper-catalyzed cyanation thus offers insights into an efficient way for the synthesis of bioactive molecules for drug discovery.

Highly stereoselective nickel-catalyzed difluoroalkylation of aryl ketones to tetrasubstituted monofluoroalkenes and quaternary alkyl difluorides.

A nickel-catalyzed difluoroalkylation of α-C-H bonds of aryl ketones to furnish highly stereo-defined tetrasubstituted monofluoroalkenes or quaternary alkyl difluorides from secondary or tertiary ketones, respectively, has been established. Mechanistic investigations indicated that these C-H fluoroalkylations proceed via a Ni(i)/Ni(iii) catalytic cycle. An obvious fluorine effect was observed in the reaction, and this reaction has demonstrated high stereoselectivity, mild conditions, and broad substrate scopes, thus enabling the late-stage fluoroalkylation of bioactive molecules. This method offers a solution for expedient construction of monofluoroalkenes from readily available materials, and provides an efficient approach for the synthesis of bioactive fluorinated compounds for the discovery of lead compounds in medicinal chemistry.

IL-10 produces a dual effect on OGD-induced neuronal apoptosis of cultured cortical neurons via the NF-κB pathway.

As a classic immunoregulatory cytokine, interleukin-10 (IL-10) can provide in vivo and in vitro neuroprotection respectively during cerebral ischemia and after the oxygen-glucose deprivation (OGD)-induced injury. However, its role in cortical neuronal survival at different post-ischemic phases remains unclear. The current study found that IL-10 had distinct effects on the neuronal apoptosis at different OGD stages: at an early stage after OGD, IL-10 promoted the OGD-induced neuronal apoptosis in the cultured primary cortical neurons by activating p65 subunit, which up-regulated Bax expression and down-regulated Bcl-xL expression; at a late OGD stage, however, it attenuated the OGD-induced neuronal apoptosis by activating c-Rel, which up-regulated Bcl-xL expression and down-regulated Bax expression. The early-stage pro-apoptosis and late-stage anti-apoptosis were both partly abolished by PDTC, an NF-κB inhibitor, and promoted by PMA, an NF-κB activator. The optimal anti-apoptotic effect appeared when the cultured neurons were treated with IL-10 at 9-24 h after OGD. Taken together, our findings suggest that IL-10 exerts a dual effect on the survival of the cultured neurons by activating the NF-κB pathway at different stages after OGD injury and that PMA treatment at a late stage can facilitate the IL-10-conferred neuroprotection against OGD-induced neuronal injury.

[Application of pulsed-field gel electrophoresis in a food-borne outbreak of Salmonella serotype Muenchen infection].

OBJECTIVE: To investigate the application of pulsed-field gel electrophoresis (PFGE) in food-borne outbreak. METHODS: Pathogens were isolated and further characteristics identified by traditional methods. The strains isolated were carried out with molecular typing with using PFGE. PFGE was performed by Laboratory Directions for molecular subtyping of Salmonella by PFGE (CDC, USA) and the results of PFGE were analyzed by BioNumerics soft. RESULTS: Totally 14 Salmonella serotype Muenchen strains were isolated from 19 patients, 3 of 9 suspicious foods were positive for S. muenchen and 7 strains were isolated from 18 cooks. The biochemistry characterization and antimicrobial susceptibility of all the strains isolated were the same. 23 S. muenchen isolates were all shown indistinguishable by PFGE. CONCLUSION: PFGE should play a key role in identifying the outbreak-associated isolates and distinguishing them from unrelated sporadic isolates. It might also demonstrate that the genetic fingerprints of serotype Muenchen isolates derived from patients were indistinguishable from those derived from drinks. PFGE might provide precise information on bacterial food-borne pathogens, promptly identify the source of infection, and effectively prevent from spreading. It should be one of the early warning method on controlling outbreak of the food-borne disease.

[Analysis of pulsed-field gel electrophoresis molecular subtyping of Shigella strains in Shenzhen].

OBJECTIVE: To analyze the genetic relations of Shigella isolated from Shenzhen in 2001-2006 and develop primary molecular subtyping surveillance network of Shigella. METHODS: Chromosomal DNAs from 55 isolated in agarose were digested with the restriction enzyme Xba I, and then were analyzed by pulsed-field gel electrophoresis. Pulsed-field gel electrophoresis (PFGE) patterns were clustered using BioNumerics software. RESULTS: All 41 distinctive PFGE patterns were identified among 55 strains. 32 strains belonged to one cluster. Differences were observed in other strains. CONCLUSION: Both genetic-related clones and non-related clones of Shigella existed in Shenzhen. The development of PFGE molecular subtyping surveillance network would contribute to the active surveillance, outbreak investigation and source tracking for Shigellosis.

Cobalt-catalyzed difluoroalkylation of tertiary aryl ketones for facile synthesis of quaternary alkyl difluorides.

The selective incorporation of gem-difluoroalkyl groups into biologically active molecules has long been used as an efficient strategy for drug design and discovery. However, the catalytic C(sp3)-CF2 bond-forming cross-coupling reaction for selective incorporation of difluoromethylene group into diverse alkyl chains, especially more sterically demanding secondary and tertiary functionalized alkanes, still remains as a major challenge. Herein, we describe a cobalt-catalyzed difluoroalkylation of tertiary aryl ketones for facile synthesis of quaternary alkyl difluorides, which exhibited high efficiency, broad scope and mild conditions. The synthetic utility of this method is demonstrated by late-stage difluoroalkylation of donepezil, a well-known acetylcholinesterase inhibitor used to treat the Alzheimer's disease. Preliminary mechanistic investigations indicate that a difluoroalkyl radical is involved in a Co(I)/Co(III) catalytic cycle. This cobalt-catalyzed fluoroalkylation thus offers insights into an efficient way for the synthesis of fluoroalkylated bioactive molecules for drug discovery.

Nickel-Catalyzed Heck-Type Monofluoroacetation of Styrenes for Facile Synthesis of Allylic Fluorides.

An efficient nickel-catalyzed Heck-type reaction between styrenes and fluoroalkyl iodine has been developed. This novel transformation has demonstrated a broad substrate scope, mild reaction conditions and excellent E-stereoselectivity. This efficient synthetic method has been applied to the late-stage monofluoroacetation of biologically active molecules. Mechanistic investigations indicate that a monofluoroalkyl radical is involved in the catalytic cycle.