Study on microbial diversity of washing machines.

Health and environmental protection are the development trend of household appliances, coupled with the impact of the COVID-19 epidemic in the past few years. Consumers have unprecedented concerns and expectations about the sterilization and disinfection functions of household appliances. As a washing and nursing equipment for household clothes, the anti-bacterial technology of washing machine has developed rapidly. The new models of washing machines in the market have basically added the function of sterilization. In order to thoroughly solve the problem of sterilization and bacteriostasis of washing machines from the source, the distribution of microbial contamination in washing machines should be fully investigated. At present, there is almost no systematic study on the microbial community structure in washing machines in China. Therefore, the purpose of this study is to analyze the bacterial community structure in Chinese household washing machines. To explore the key factors affecting the bacterial community structure of washing machines. Bacterial communities were comprehensively analyzed by high throughput sequencing. Using chao and shannon indexes as indicators, one-way ANOVA was used to explore the key factors affecting the bacterial community structure of washing machines. A total of 2,882,778 tags and 21,265 OTUs from 522 genera were sequenced from 56 washing machine samples. Genus Mycobacterium, Pseudomonas, Brevundimonas, Sphingomonas, Sphingobium, Enhydrobacter, Methylobacterium, Pseudoxanthomonas, Stenotrophomonas and Sphingopyxis were the top ten bacteria genera in abundance. The effects of sources, types, frequency of utilization, sampling locations and service life of washing machines on bacterial diversity in washing machine were systematically analyzed. The statistical analysis showed that service life was an important factor affecting bacterial diversity in washing machine. Our study lays a foundation for directional screening of characteristic microorganisms with targeted characters including malodor-producing, fouling, pathogenic and stress-resistance, the antibacterial evaluation, metabolic mechanism of key characteristic microorganisms as well as antibacterial materials development. At present, the sterilization technology of washing machines has not been fully in combination with the distribution survey of microorganisms in washing machines. According to the specific microorganism distribution condition of the washing machine, the key distribution positions and the types of specific microorganisms contained in different positions, conduct more targeted sterilization treatment. This will help to completely solve the problem of microbial growth in washing machines from the source.

Ferrocene-functionalized zirconium-oxo clusters for achieving high-performance thermocatalytic redox reactions.

The Zr(IV) ions are easily hydrolyzed to form oxides, which severely limits the discovery of new structures and applications of Zr-based compounds. In this work, three ferrocene (Fc)-functionalized Zr-oxo clusters (ZrOCs), Zr9Fc6, Zr10Fc6 and Zr12Fc8 were synthesized through inhibiting the hydrolysis of Zr(IV) ions, which show increased nuclearity and regular structural variation. More importantly, these Fc-functionalized ZrOCs were used as heterogeneous catalysts for the transfer hydrogenation of levulinic acid (LA) and phenol oxidation reactions for the first time, and displayed outstanding catalytic activity. In particular, Zr12Fc8 with the largest number of Zr active sites and Fc groups can achieve > 95% yield for LA-to-γ-valerolactone within 4 h (130 °C) and > 98% yield for 2,3,6-trimethylphenol-to-2,3,5-trimethyl-p-benzoquinone within 30 min (80 °C), showing the best catalytic performance. Catalytic characterization combined with theory calculations reveal that in the Fc-functionalized ZrOCs, the Zr active sites could serve as substrate adsorption sites, while the Fc groups could act as hydrogen transfer reagent or Fenton reagent, and thus achieve effectively intramolecular metal-ligand synergistic catalysis. This work develops functionalized ZrOCs as catalysts for thermal-triggered redox reactions.

Fermentation Characteristics of Fermented Milk with Streptococcus thermophilus CICC 6063 and Lactobacillus helveticus CICC 6064 and Volatile Compound Dynamic Profiles during Fermentation and Storage.

The lactic acid bacteria Streptococcus thermophilus and Lactobacillus helveticus are commonly used as starter cultures in dairy product production. This study aimed to investigate the characteristics of fermented milk using different ratios of these strains and analyze the changes in volatile compounds during fermentation and storage. A 10:1 ratio of Streptococcus thermophilus CICC 6063 to Lactobacillus helveticus CICC 6064 showed optimal fermentation time (4.2 h), viable cell count (9.64 log10 colony-forming units/mL), and sensory evaluation score (79.1 points). In total, 56 volatile compounds were identified and quantified by solid-phase microextraction and gas chromatography-mass spectrometry (SPME-GC-MS), including aldehydes, ketones, acids, alcohols, esters, and others. Among these, according to VIP analysis, 2,3-butanedione, acetoin, 2,3-pentanedione, hexanoic acid, acetic acid, acetaldehyde, and butanoic acid were identified as discriminatory volatile metabolites for distinguishing between different time points. Throughout the fermentation and storage process, the levels of 2,3-pentanedione and acetoin exhibited synergistic dynamics. These findings enhance our understanding of the chemical and molecular characteristics of milk fermented with Streptococcus thermophilus and Lactobacillus helveticus, providing a basis for improving the flavor and odor of dairy products during fermentation and storage.

Identification and quantification of viable Lacticaseibacillus rhamnosus in probiotics using validated PMA-qPCR method.

The identification and quantification of viable bacteria at the species/strain level in compound probiotic products is challenging now. Molecular biology methods, e.g., propidium monoazide (PMA) combination with qPCR, have gained prominence for targeted viable cell counts. This study endeavors to establish a robust PMA-qPCR method for viable Lacticaseibacillus rhamnosus detection and systematically validated key metrics encompassing relative trueness, accuracy, limit of quantification, linear, and range. The inclusivity and exclusivity notably underscored high specificity of the primers for L. rhamnosus, which allowed accurate identification of the target bacteria. Furthermore, the conditions employed for PMA treatment were fully verified by 24 different L. rhamnosus including type strain, commercial strains, etc., confirming its effective discrimination between live and dead bacteria. A standard curve constructed by type strain could apply to commercial strains to convert qPCR Cq values to viable cell numbers. The established PMA-qPCR method was applied to 46 samples including pure cultures, probiotics as food ingredients, and compound probiotic products. Noteworthy is the congruity observed between measured and theoretical values within a 95% confidence interval of the upper and lower limits of agreement, demonstrating the relative trueness of this method. Moreover, accurate results were obtained when viable L. rhamnosus ranging from 103 to 108 CFU/mL. The comprehensive appraisal of PMA-qPCR performances provides potential industrial applications of this new technology in quality control and supervision of probiotic products.

Development of a SNP-based strain-identified method for Streptococcus thermophilus CICC 6038 and Lactobacillus delbrueckii ssp. bulgaricus CICC 6047 using pan-genomics analysis.

The health benefits conferred by probiotics is specific to individual probiotic strains, highlighting the importance of identifying specific strains for research and production purposes. Streptococcus thermophilus CICC 6038 and Lactobacillus delbrueckii ssp. bulgaricus CICC 6047 are exceedingly valuable for commercial use with an excellent mixed-culture fermentation. To differentiate these 2 strains from other S. thermophilus and L. delbrueckii ssp. bulgaricus, a specific, sensitive, accurate, rapid, convenient, and cost-effective method is required. In this study, we conducted a pan-genome analysis of S. thermophilus and L. delbrueckii ssp. bulgaricus to identify species-specific core genes, along with strain-specific single-nucleotide polymorphisms (SNPs). These genes were used to develop suitable PCR primers, and the conformity of sequence length and unique SNPs was confirmed by sequencing for qualitative identification at the strain level. The results demonstrated that SNPs analysis of PCR products derived from these primers could distinguish CICC 6038 and CICC 6047 accurately and reproducibly from the other strains of S. thermophilus and L. delbrueckii ssp. bulgaricus, respectively. The strain-specific PCR method based on SNPs herein is universally applicable for probiotics identification. It offers valuable insights into identifying probiotics at the strain level that is fit-for-purpose in quality control and compliance assessment of commercial dairy products.

Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis.

BACKGROUND: Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology. METHODS: The study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted. RESULTS: The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12-0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05-0.92, P = 0.037). CONCLUSIONS: We quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.

Fermentation characteristics and postacidification of yogurt by Streptococcus thermophilus CICC 6038 and Lactobacillus delbrueckii ssp. bulgaricus CICC 6047 at optimal inoculum ratio.

This study aimed to investigate the symbiosis between Streptococcus thermophilus CICC 6038 and Lactobacillus delbrueckii ssp. bulgaricus CICC 6047. In addition, the effect of their different inoculum ratios was determined, and comparison experiments of fermentation characteristics and storage stability of milk fermented by their monocultures and cocultures at optimal inoculum ratio were performed. We found the time to obtain pH 4.6 and ΔpH during storage varied among 6 inoculum ratios (1:1, 2:1, 10:1, 19:1, 50:1, 100:1). By the statistical model to evaluate the optimal ratio, the ratio of 19:1 was selected, which exhibited high acidification rate and low postacidification with pH values remaining between 4.2 and 4.4 after a 50-d storage. Among the 3 groups included in our analyses (i.e., the monocultures of S. thermophilus CICC 6038 [St] and Lb. bulgaricus CICC 6047 [Lb] and their cocultures [St+Lb] at 19:1), the coculture group showed higher acidification activity, improved rheological properties, richer typical volatile compounds, more desirable sensor quality after the fermentation process than the other 2 groups. However, the continuous accumulation of acetic acid during storage showed that acetic acid was more highly correlated with postacidification than d-lactic acid for the Lb group and St+Lb group. Our study emphasized the importance of selecting an appropriate bacterial consortium at the optimal inoculum ratio to achieve favorable fermentation performance and enhanced postacidification stability during storage.

Thrombospondin 1 and Reelin act through Vldlr to regulate cardiac growth and repair.

Adult mammalian cardiomyocytes have minimal cell cycle capacity, which leads to poor regeneration after cardiac injury such as myocardial infarction. Many positive regulators of cardiomyocyte cell cycle and cardioprotective signals have been identified, but extracellular signals that suppress cardiomyocyte proliferation are poorly understood. We profiled receptors enriched in postnatal cardiomyocytes, and found that very-low-density-lipoprotein receptor (Vldlr) inhibits neonatal cardiomyocyte cell cycle. Paradoxically, Reelin, the well-known Vldlr ligand, expressed in cardiac Schwann cells and lymphatic endothelial cells, promotes neonatal cardiomyocyte proliferation. Thrombospondin1 (TSP-1), another ligand of Vldlr highly expressed in adult heart, was then found to inhibit cardiomyocyte proliferation through Vldlr, and may contribute to Vldlr's overall repression on proliferation. Mechanistically, Rac1 and subsequent Yap phosphorylation and nucleus translocation mediate the regulation of the cardiomyocyte cell cycle by TSP-1/Reelin-Vldlr signaling. Importantly, Reln mutant neonatal mice displayed impaired cardiomyocyte proliferation and cardiac regeneration after apical resection, while cardiac-specific Thbs1 deletion and cardiomyocyte-specific Vldlr deletion promote cardiomyocyte proliferation and are cardioprotective after myocardial infarction. Our results identified a novel role of Vldlr in consolidating extracellular signals to regulate cardiomyocyte cell cycle activity and survival, and the overall suppressive TSP-1-Vldlr signal may contribute to the poor cardiac repair capacity of adult mammals.

An artificial intelligence-based ecological index for prognostic evaluation of colorectal cancer.

BACKGROUND AND OBJECTIVE: In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in predicting the prognosis of colorectal cancer. This study introduces a novel approach based on artificial intelligence (AI) and immunohistochemistry (IHC)-stained whole-slide images (WSIs) of colorectal cancer (CRC) patients to quantitatively assess the spatial associations between tumor cells and immune cells. To achieve this, we employ the Morisita-Horn ecological index (Mor-index), which allows for a comprehensive analysis of the spatial distribution patterns between tumor cells and immune cells within the TME. MATERIALS AND METHODS: In this study, we employed a combination of deep learning technology and traditional computer segmentation methods to accurately segment the tumor nuclei, immune nuclei, and stroma nuclei within the tumor regions of IHC-stained WSIs. The Mor-index was used to assess the spatial association between tumor cells and immune cells in TME of CRC patients by obtaining the results of cell nuclei segmentation. A discovery cohort (N = 432) and validation cohort (N = 137) were used to evaluate the prognostic value of the Mor-index for overall survival (OS). RESULTS: The efficacy of our method was demonstrated through experiments conducted on two datasets comprising a total of 569 patients. Compared to other studies, our method is not only superior to the QuPath tool but also produces better segmentation results with an accuracy of 0.85. Mor-index was quantified automatically by our method. Survival analysis indicated that the higher Mor-index correlated with better OS in the discovery cohorts (HR for high vs. low 0.49, 95% CI 0.27-0.77, P = 0.0014) and validation cohort (0.21, 0.10-0.46, < 0.0001). CONCLUSION: This study provided a novel AI-based approach to segmenting various nuclei in the TME. The Mor-index can reflect the immune status of CRC patients and is associated with favorable survival. Thus, Mor-index can potentially make a significant role in aiding clinical prognosis and decision-making.

Structural Evolution of Giant Polyoxometalate: From "Keplerate" to "Lantern" Type Mo132 for Improved Oxidation Catalysis.

Structural variants of high-nuclearity clusters are extremely important for their modular assembly study and functional expansion, yet the synthesis of such giant structural variants remains a great challenge. Herein, we prepared a lantern-type giant polymolybdate cluster (L-Mo132 ) containing equal metal nuclearity with the famous Keplerate type Mo132 (K-Mo132 ). The skeleton of L-Mo132 features a rare truncated rhombic triacontrahedron, which is totally different with the truncated icosahedral K-Mo132 . To the best of our knowledge, this is the first time to observe such structural variants in high-nuclearity cluster built up of more than 100 metal atoms. Scanning transmission electron microscopy reveals that L-Mo132 has good stability. More importantly, because the pentagonal [Mo6 O27 ]n- building blocks in L-Mo132 are concave instead of convex in the outer face, it contains multiple terminal coordinated water molecules on its outer surface, which make it expose more active metal sites to display superior phenol oxidation performance, which is more higher than that of K-Mo132 coordinated in M=O bonds on the outer surface.

Desmoplastic Reaction Associates with Prognosis and Adjuvant Chemotherapy Response in Colorectal Cancer: A Multicenter Retrospective Study.

Desmoplastic reaction (DR) is one of many tumor-host interactions and is associated with the overall survival (OS) of patients with colorectal cancer. However, the clinical significance of DR requires further study in large multicenter cohorts and its predictive value in adjuvant chemotherapy (ACT) response remains unclear. Here, a total of 2,225 patients with colorectal cancer from five independent institutions were divided into primary (N = 1,012 from two centers) and validation (N = 1,213 from three centers) cohorts. DR was classified as immature, middle, or mature depending on the presence of myxoid stroma and hyalinized collagen bundles at the invasive front of the primary tumor. OS among different subgroups were compared, and the correlations of DR type with tumor-infiltrating lymphocytes (TILs) within stroma, tumor stroma ratio (TSR), and Stroma AReactive Invasion Front Areas (SARIFA) were also analyzed. In the primary cohort, patients with mature DR had the highest 5-year survival rate. These findings were confirmed in validation cohort. In addition, for stage II colorectal cancer, patients classified as non-mature DR would benefit from ACT compared with surgery alone. Furthermore, immature and middle DR were more associated with high TSR, less distribution of TILs within stroma and positive SARIFA compared with mature. Taken together, these data suggest that DR is a robust-independent prognostic factor for patients with colorectal cancer. For patients with stage II colorectal cancer, non-mature DR could be a potential marker for recognizing high-risk patients who may benefit from ACT. Significance: DR has the potential to identify patients with high-risk colorectal cancer and predict the efficacy of adjuvant chemotherapy in patients with stage II colorectal cancer. Our findings support reporting DR types as additional pathologic parameters in clinical practice for more precise risk stratification.

Dispersal Behavior Characters of Spodoptera frugiperda Larvae.

The fall armyworm (FAW), Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), is a major pest of corn worldwide. FAW larval dispersal is an important life strategy that influences FAW population distribution in corn fields and subsequent plant damage. We studied FAW larval dispersal in the laboratory with sticky plates placed around the test plant and a unidirectional airflow source. Crawling and ballooning were the main dispersal means of FAW larvae both within and between corn plants. All larval instars (1st-6th) could disperse by crawling, with crawling being the only dispersal mechanism for 4th-6th instars. By crawling, FAW larvae could reach all aboveground parts of a corn plant as well as adjacent corn plants where leaves overlapped. Ballooning was used primarily by 1st-3rd instar larvae, and the proportion of these larvae that used ballooning decreased with age. Ballooning was largely governed by the larva's interaction with airflow. Airflow influenced the direction and distance of larval ballooning. With an airflow speed of about 0.05 m/s, 1st instars could travel up to 196 cm from the test plant, indicating that long-distance FAW larval dispersal depends on ballooning. These results increase our understanding of FAW larval dispersal and provide scientific information for the development of FAW monitoring and control strategies.

Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer.

BACKGROUND: In colorectal cancer (CRC), both tumor invasion and immunological analysis at the tumor invasive margin (IM) are significantly associated with patient prognosis, but have traditionally been reported independently. We propose a new scoring system, the TGP-I score, to assess the association and interactions between tumor growth pattern (TGP) and tumor infiltrating lymphocytes at the IM and to predict its prognostic validity for CRC patient stratification. MATERIALS AND METHODS: The types of TGP were assessed in hematoxylin and eosin-stained whole-slide images. The CD3+ T-cells density at the IM was automatically quantified on immunohistochemical-stained slides using a deep learning method. A discovery (N = 347) and a validation (N = 132) cohorts were used to evaluate the prognostic value of the TGP-I score for overall survival. RESULTS: The TGP-I score3 (trichotomy) was an independent prognostic factor, with higher TGP-I score3 associated with worse prognosis in the discovery (unadjusted hazard ratio [HR] for high vs. low 3.62, 95% confidence interval [CI] 2.22-5.90; p < 0.001) and validation cohort (unadjusted HR for high vs. low 5.79, 95% CI 1.84-18.20; p = 0.003). The relative contribution of each parameter to predicting survival was analyzed. The TGP-I score3 had similar importance compared to tumor-node-metastasis staging (31.2% vs. 32.9%) and was stronger than other clinical parameters. CONCLUSIONS: This automated workflow and the proposed TGP-I score could further provide accurate prognostic stratification and have potential value for supporting the clinical decision-making of stage I-III CRC patients.Key messagesA new scoring system, the TGP-I score, was proposed to assess the association and interactions of TGP and TILs at the tumor invasive margin.TGP-I score could be an independent predictor of prognosis for CRC patients, with higher scores being associated with worse survival.TGP-I score had similar importance compared to tumor-node-metastasis staging and was stronger than other clinical parameters.

Necrosis score as a prognostic factor in stage I-III colorectal cancer: a retrospective multicenter study.

BACKGROUND: Tumor necrosis results from failure to meet the requirement for rapid proliferation of tumor, related to unfavorable prognosis in colorectal cancer (CRC). However, previous studies used traditional microscopes to evaluate necrosis on slides, lacking a simultaneous phase and panoramic view for assessment. Therefore, we proposed a whole-slide images (WSIs)-based method to develop a necrosis score and validated its prognostic value in multicenter cohorts. METHODS: Necrosis score was defined as the proportion of necrosis in the tumor area, semi-quantitatively classified into 3-level score groups by the cut-off of 10% and 30% on HE-stained WSIs. 768 patients from two centers were enrolled in this study, divided into a discovery (N = 445) and a validation (N = 323) cohort. The prognostic value of necrosis score was evaluated by Kaplan-Meier curves and the Cox model. RESULT: Necrosis score was associated with overall survival, with hazard ratio for high vs. low in discovery and validation cohorts being 2.62 (95% confidence interval 1.59-4.32) and 2.51 (1.39-4.52), respectively. The 3-year disease free survival rates of necrosis-low, middle, and high were 83.6%, 80.2%, and 59.8% in discovery cohort, and 86.5%, 84.2%, and 66.5% in validation cohort. In necrosis middle plus high subgroup, there was a trend but no significant difference in overall survival between surgery alone and adjuvant chemotherapy group in stage II CRC (P = .075). CONCLUSION: As a stable prognostic factor, high-level necrosis evaluated by the proposed method on WSIs was associated with unfavorable outcomes. Additionally, adjuvant chemotherapy provide survival benefits for patients with high necrosis in stage II CRC.

Development and validation of a scoring system incorporating tumor growth pattern and perineural invasion for risk stratification in colorectal cancer.

Tumor growth pattern (TGP) and perineural invasion (PNI) at the invasive margin have been recognized as indicators of tumor invasiveness and prognostic events in colorectal cancer (CRC). This study aims to develop a scoring system incorporating TGP and PNI, and further investigate its prognostic significance for CRC risk stratification. A scoring system, termed tumor-invasion score, was established by summing TGP and PNI scores. The discovery cohort (N = 444) and the validation cohort (N = 339) were used to explore the prognostic significance of the tumor-invasion score. The endpoints of the event were disease-free survival (DFS) and overall survival (OS) which were analyzed by the Cox proportional hazard model. In the discovery cohort, Cox regression analysis showed that DFS and OS were inferior for score 4 group compared with score 1 group (DFS, hazard ratio (HR) 4.44, 95% confidence interval (CI) 2.49-7.92, p < 0.001; OS, 4.41, 2.37-8.19,p < 0.001). The validation cohort showed similar results (DFS, 4.73, 2.39-9.37, p < 0.001; OS, 5.52, 2.55-12.0, p < 0.001). The model combining tumor-invasion score and clinicopathologic information showed good discrimination performance than single predictors. TGP and PNI were associated with tumor invasiveness and survival in CRC. The tumor-invasion score generated by TGP and PNI scores served as an independent prognostic parameter of DFS and OS for CRC patients.

Salmonella effector SopB reorganizes cytoskeletal vimentin to maintain replication vacuoles for efficient infection.

A variety of intracellular bacteria modulate the host cytoskeleton to establish subcellular niches for replication. However, the role of intermediate filaments, which are crucial for mechanical strength and resilience of the cell, and in bacterial vacuole preservation remains unclear. Here, we show that Salmonella effector SopB reorganizes the vimentin network to form cage-like structures that surround Salmonella-containing vacuoles (SCVs). Genetic removal of vimentin markedly disrupts SCV organization, significantly reduces bacterial replication and cell death. Mechanistically, SopB uses its N-terminal Cdc42-binding domain to interact with and activate Cdc42 GTPase, which in turn recruits vimentin around SCVs. A high-content imaging-based screening identified that MEK1/2 inhibition led to vimentin dispersion. Our work therefore elucidates the signaling axis SopB-Cdc42-MEK1/2 as mobilizing host vimentin to maintain concrete SCVs and identifies a mechanism contributing to Salmonella replication. Importantly, Trametinib, a clinically-approved MEK1/2 inhibitor identified in the screen, displayed significant anti-infection efficacy against Salmonella both in vitro and in vivo, and may provide a therapeutic option for treating drug-tolerant salmonellosis.

CO2 Photoactivation Study of Adenine Nucleobase: Role of Hydrogen-Bonding Traction.

The discovery and in-depth study of non-biocatalytic applications of active biomolecules are essential for the development of biomimicry. Here, the effect of intermolecular hydrogen-bonding traction on the CO2 photoactivation performance of adenine nucleobase by means of an adenine-containing model system (AMOF-1-4) is uncovered. Remarkably, the hydrogen-bonding schemes around adenines are regularly altered with the increase in the alkyl (methyl, ethyl, isopropyl, and tert-butyl) electron-donating capacity of the coordinated aliphatic carboxylic acids, and thus, lead to a stepwise improvement in CO2 photoreduction activity. Density functional theory calculations demonstrate that strong intermolecular hydrogen-bonding traction surrounding adenine can obviously increase the adenine-CO2 interaction energy and, therefore, result in a smoother CO2 activation process. Significantly, this work also provides new inspiration for expanding the application of adenine to more small-molecule catalytic reactions.

Kingella pumchi sp. nov., an organism isolated from human vertebral puncture tissue.

A Gram-negative, non-motile rod and strictly aerobic bacterium, designated as 18B16333T, was isolated from vertebral puncture tissue of a patient at Peking union medical college hospital in China. Growth occurred in NaCl concentrations of 0-1% (w/v) (optimum growth at 0% NaCl), at temperatures of 25-40 °C (optimum growth at 37 °C) and at pH 6.0-9.0 (optimum growth at pH 8.0). Diphosphatidylglycerol, phosphatidylglycerol and phosphatidylethanolamine were the predominant polar lipids, and the major fatty acids were C16:0, C18:1 ω7c/C18:1 ω6c and C16:1 ω7c/C16:1 ω6c. Phylogenetic analysis based on 16S rRNA gene sequence comparisons indicated that strain 18B16333T was most closely related to Kingella potus CCUG 49773 T (97.3%, 16S rRNA gene sequence identity) and Neisseria bacilliformis CCUG 50858 T (96.8%). The ANI values between strain 18B16333T and the type strains K. potus CCUG 49773 T, N. bacilliformis CCUG 50858 T, Kingella kingae CCUG 352 T and Neisseria gonorrhoeae CCUG 26876 T were 77.3%, 79.1%, 72.1% and 75.4%, respectively. The dDDH values between strain 18B16333T and the four reference strains mentioned above were 24.8%, 26.9%, 24.2% and 20.7%. Further core gene analysis distinctively clustered strain 18B16333T with four Kingella species but not with Neisseria species. Based on the phenotypic, chemotaxonomic, and phylogenetic properties, strain 18B16333T represents a novel species of the genus Kingella, for which the name Kingella pumchi sp. nov. is proposed. The type strain is Kingella pumchi 18B16333T (= CICC 24913 T = CCUG 75125 T).

Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer.

BACKGROUND: We proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer. METHODS: A total of 1010 colorectal cancer patients from three centers were enrolled in this retrospective study, divided into a primary (N = 544) and a validation cohort (N = 466). We proposed the Deep-immune score, which reflected both tumor stroma proportion and the infiltration of immune cells in the stroma region. We further analyzed the correlation between the score and CD3+ T cells density in the stroma region using immunohistochemistry-stained whole-slide images. Survival analysis was performed using the Cox proportional hazard model, and the endpoint of the event was the overall survival. RESULT: Patients were classified into 4-level score groups (score 1-4). A high Deep-immune score was associated with a high level of CD3+ T cells infiltration in the stroma region. In the primary cohort, survival analysis showed a significant difference in 5-year survival rates between score 4 and score 1 groups: 87.4% vs. 58.2% (Hazard ratio for score 4 vs. score 1 0.27, 95% confidence interval 0.15-0.48, P < 0.001). Similar trends were observed in the validation cohort (89.8% vs. 67.0%; 0.31, 0.15-0.62, < 0.001). Stratified analysis showed that the Deep-immune score could distinguish high-risk and low-risk patients in stage II colorectal cancer (P = 0.018). CONCLUSION: The proposed Deep-immune score quantified by artificial intelligence can reflect the immune status of patients with colorectal cancer and is associate with favorable survival. This digital pathology-based finding might advocate change in risk stratification and consequent precision medicine.

Artificial intelligence for quantifying Crohn's-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancer.

Crohn's-like lymphoid reaction (CLR) and tumor-infiltrating lymphocytes (TILs) are crucial for the host antitumor immune response. We proposed an artificial intelligence (AI)-based model to quantify the density of TILs and CLR in immunohistochemical (IHC)-stained whole-slide images (WSIs) and further constructed the CLR-I (immune) score, a tissue level- and cell level-based immune factor, to predict the overall survival (OS) of patients with colorectal cancer (CRC). The TILs score and CLR score were obtained according to the related density. And the CLR-I score was calculated by summing two scores. The development (Hospital 1, N = 370) and validation (Hospital 2 & 3, N = 144) cohorts were used to evaluate the prognostic value of the CLR-I score. The C-index and integrated area under the curve were used to assess the discrimination ability. A higher CLR-I score was associated with a better prognosis, which was identified by multivariable analysis in the development (hazard ratio for score 3 vs score 0 = 0.22, 95% confidence interval 0.12-0.40, P < 0.001) and validation cohort (0.21, 0.05-0.78, P = 0.020). The AI-based CLR-I score outperforms the single predictor in predicting OS which is objective and more prone to be deployed in clinical practice.