Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery.

Objective: By constructing a predictive model using machine learning and deep learning technologies, we aim to understand the risk factors for postoperative intestinal obstruction in laparoscopic colorectal cancer patients, and establish an effective artificial intelligence-based predictive model to guide individualized prevention and treatment, thus improving patient outcomes. Methods: We constructed a model of the artificial intelligence algorithm in Python. Subjects were randomly assigned to either a training set for variable identification and model construction, or a test set for testing model performance, at a ratio of 7:3. The model was trained with ten algorithms. We used the AUC values of the ROC curves, as well as accuracy, precision, recall rate and F1 scores. Results: The results of feature engineering composited with the GBDT algorithm showed that opioid use, anesthesia duration, and body weight were the top three factors in the development of POI. We used ten machine learning and deep learning algorithms to validate the model, and the results were as follows: the three algorithms with best accuracy were XGB (0.807), Decision Tree (0.807) and Neural DecisionTree (0.807); the two algorithms with best precision were XGB (0.500) and Decision Tree (0.500); the two algorithms with best recall rate were adab (0.243) and Decision Tree (0.135); the two algorithms with highest F1 score were adab (0.290) and Decision Tree (0.213); and the three algorithms with best AUC were Gradient Boosting (0.678), XGB (0.638) and LinearSVC (0.633). Conclusion: This study shows that XGB and Decision Tree are the two best algorithms for predicting the risk of developing ileus after laparoscopic colon cancer surgery. It provides new insight and approaches to the field of postoperative intestinal obstruction in colorectal cancer through the application of machine learning techniques, thereby improving our understanding of the disease and offering strong support for clinical decision-making.

A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm.

We constructed an early prediction model for postoperative pulmonary complications after thoracoscopic surgery using machine learning and deep learning algorithms. The artificial intelligence prediction models were built in Python, primarily using artificial intelligencealgorithms including both machine learning and deep learning algorithms. Correlation analysis showed that postoperative pulmonary complications were positively correlated with age and surgery duration, and negatively correlated with serum albumin. Using the light gradient boosting machine(LGBM) algorithm, weighted feature engineering revealed that single lung ventilation duration, history of smoking, surgery duration, ASA score, and blood glucose were the main factors associated with postoperative pulmonary complications. Results of artificial intelligence algorithms for predicting pulmonary complications after thoracoscopy in the test group: In terms of accuracy, the two best algorithms were Logistic Regression (0.831) and light gradient boosting machine(0.827); in terms of precision, the two best algorithms were Gradient Boosting (0.75) and light gradient boosting machine (0.742); in terms of recall, the three best algorithms were gaussian naive bayes (0.581), Logistic Regression (0.532), and pruning Bayesian neural network (0.516); in terms of F1 score, the two best algorithms were LogisticRegression (0.589) and pruning Bayesian neural network (0.566); and in terms of Area Under Curve(AUC), the two best algorithms were light gradient boosting machine(0.873) and pruning Bayesian neural network (0.869). The results of this study suggest that pruning Bayesian neural network (PBNN) can be used to assess the possibility of pulmonary complications after thoracoscopy, and to identify high-risk groups prior to surgery.

Nitric oxide and ion channels mediate L-cysteine-induced inhibition of colonic smooth muscle contraction.

Previous studies have suggested that L-cysteine regulates gut motility through hydrogen sulfide. However, the mechanisms involved in the L-cysteine-induced response have not been extensively studied. This study aimed to investigate the underlying mechanisms of action of L-cysteine on spontaneous contraction of rat colon. Longitudinal and circular muscle strips from rat middle colon were prepared to measure the spontaneous contractile activities of colon in an organ bath system. Whole-cell voltage-clamp techniques were applied to record the currents of L-type voltage-dependent Ca2+ channels (VDCCs) and voltage-gated K+ channels (Kv) in isolated smooth muscle cells (SMCs) from colon. L-cysteine inhibited the spontaneous contraction of longitudinal and circular muscle strips from the rat colon in a concentration-dependent manner. The inhibition induced by L-cysteine was significantly decreased by inhibitors of H2S synthesis (p < 0.05). Furthermore, the suppression induced by L-cysteine was partially attenuated by tetrodotoxin, L-NNA and glibenclamide (p < 0.05). Whole-cell voltage-clamp recordings showed that L-cysteine caused a remarkable reduction in the peak currents of VDCCs and significantly increased the membrane currents of Kv channels in isolated SMCs (p < 0.05). We concluded that L-cysteine inhibits the contractile activities of smooth muscle strips from the rat colon. The relaxation in response to L-cysteine may be in part mediated by a nitrergic pathway and by inhibiting the VDCCs in combination with a direct activation of the KV channels and KATP channels.

Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms.

OBJECTIVE: PONV reduces patient satisfaction and increases hospital costs as patients remain in the hospital for longer durations. In this study, we build a preliminary artificial intelligence algorithm model to predict early PONV in patients. METHODS: We use R for statistical analysis and Python for the machine learning prediction model. RESULTS: Average characteristic engineering results showed that haloperidol, sex, age, history of smoking, and history of PONV were the first 5 contributing factors in the occurrence of early PONV. Test group results for artificial intelligence prediction of early PONV: in terms of accuracy, the four best algorithms were CNNRNN (0.872), Decision Tree (0.868), SVC (0.866) and adab (0.865); in terms of precision, the three best algorithms were CNNRNN (1.000), adab (0.400) and adab (0.868); in terms of AUC, the top three algorithms were Logistic Regression (0.732), SVC (0.731) and adab (0.722). Finally, we built a website to predict early PONV online using the Streamlit app on the following website: ( https://zhouchengmao-streamlit-app-lsvc-ad-st-app-lsvc-adab-ponv-m9ynsb.streamlit.app/ ). CONCLUSION: Artificial intelligence algorithms can predict early PONV, whereas logistic regression, SVC and adab were the top three artificial intelligence algorithms in overall performance. Haloperidol, sex, age, smoking history, and PONV history were the first 5 contributing factors associated with early PONV.

Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms.

OBJECTIVE: We tested the performance of general machine learning and joint machine learning algorithms in the classification of bone metastasis, in patients with lung adenocarcinoma. METHODS: We used R version 3.5.3 for statistical analysis of the general information, and Python to construct machine learning models. RESULTS: We first used the average classifiers of the 4 machine learning algorithms to rank the features and the results showed that race, sex, whether they had surgery and marriage were the first 4 factors affecting bone metastasis. Machine learning results in the training group: for area under the curve (AUC), except for RF and LR, the AUC values of all machine learning classifiers were greater than .8, but the joint algorithm did not improve the AUC for any single machine learning algorithm. Among the results related to accuracy and precision, the accuracy of other machine learning classifiers except the RF algorithm was higher than 70%, and only the precision of the LGBM algorithm was higher than 70%. Machine learning results in the test group: Similarly, for areas under the curve (AUC), except RF and LR, the AUC values for all machine learning classifiers were greater than .8, but the joint algorithm did not improve the AUC value for any single machine learning algorithm. For accuracy, except for the RF algorithm, the accuracy of other machine learning classifiers was higher than 70%. The highest precision for the LGBM algorithm was .675. CONCLUSION: The results of this concept verification study show that machine learning algorithm classifiers can distinguish the bone metastasis of patients with lung cancer. This will provide a new research idea for the future use of non-invasive technology to identify bone metastasis in lungcancer. However, more prospective multicenter cohort studies are needed.

Laryngeal contact granuloma after radiotherapy in patients with nasopharyngeal carcinoma: a case series.

Background: Laryngeal contact granuloma (LCG) is a benign hypertrophic lesion and phonatory injury after abnormal vocal behavior is regarded as its major etiology. Patients receiving radiation for non-laryngeal head and neck tumors are troubled by persistent voice impairment. The occurrence of LCG after radiotherapy for nasopharyngeal carcinoma (NPC) in our practice has implored us to re-exam their underlying etiology. We hypothesize that a proportion of LCG results from voice change caused by non-laryngeal head and neck cancer radiotherapy and firstly describe a distinct LCG population originated after radiotherapy for NPC with respect to the clinical profile, presentation, prognosis and response to treatment of patients. Methods: We retrospectively reviewed the laryngoscopic examination and tumor study findings to elucidate the common clinical features of patients who presented with LCG after radiotherapy for NPC. All patients were regularly monitored with telescopic examination until lesions disappeared. Data on age, sex, clinical presentation, telescopic findings, management, latency time of lesion formation, remission time and clinical outcome were reviewed. Results: The medical review identified 27 cases of LCG secondary to radiotherapy for NPC. All lesions had been diagnosed during routine endoscopy following radiation. The interval between radiation onset and endoscopic diagnosis was 3.77 months (range, 0.67-11 months). 20 cases were resolved through simple observation, 4 cases were resolved with the administration of proton pump inhibitors (PPIs), and 3 cases with a poor response to PPI therapy required subsequent surgical resection. The mean remission time in the observation and PPI groups was 4.42 months (range, 0.73-18.9 months) and 5.78 months (range, 2.17-14.63 months), respectively. All patients recovered completely and none experienced recurrence during a mean follow-up of 32.44 months (range, 5.6-71.67 months). Conclusions: Iatrogenic granulomas of vocal process are presenting after radiation for non-laryngeal head and neck cancers. In contrast with spontaneous granulomas, these granulomas can be cured at high remission rates and low recurrence trend without specific intervention. Thus, simple observation may be sufficient for radiation-induced LCG.

Replication Origin Deletion Enhances Poly(3-Hydroxybutyrate-co-3-Hydroxyvalerate) Synthesis in Haloarchaea.

Although the use of multiple replication origins for chromosome replication has been widely characterized in haloarchaea, whether it is possible to manipulate the chromosome copy number by their genetic engineering is not known, and how it would affect the cell functioning is poorly understood. Here, we demonstrate that deletion of the three active chromosomal origins in Haloferax mediterranei remarkably reduces its DNA amounts and ploidy numbers. Consequently, the mutant strain H. mediterranei Δ123 is more sensitive to UV and mitomycin C. Surprisingly, the cell size increases by 21.2%, and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) production in shake flask culture enhances from 7.23 to 8.11 g/L in ΔEPSΔ123, although there is also a decrease in cell growth. In this mutant, the chromosomal copy number decreases, whereas the pha-encoding pHM300 megaplasmid copy number increases. Moreover, our transcriptome analysis reveals that the genes involved in primary metabolisms are significantly downregulated in ΔEPSΔ123, whereas those responsible for starch utilization and precursor supplying for PHBV monomers are upregulated. This indicates that more energy and carbon flux is redirected from primary metabolism to PHBV synthesis, thereby enhancing its PHBV accumulation. These findings may therefore provide a rational design to enhance PHBV synthesis by simply tuning the replication origins to modulate the chromosome/megaplasmid copy number ratio and subsequently influence cellular metabolism and physiological functions. IMPORTANCE The haloarchaeon Haloferax mediterranei is a potential producer of PHBV (100% biodegradable plastic) from inexpensive carbon sources. We previously reported that H. mediterranei possessed three active chromosomal origins and, when these origins were deleted, a dormant origin was activated to initiate the replication of chromosome. In this context, in the present study, we first found a close connection between replication initiation and PHBV accumulation. We describe the potential industrial advantages of the strain H. mediterranei ΔEPSΔ123, which includes the enlargement of cell volume by 21.2% and enhancement of PHBV production by 11.2%. We further reveal the possible mechanism that contributes to the greater PHBV production in the ΔEPSΔ123 strain. Overall, we provide here a conceptual advance in the field of synthetic biology by modulating chromosome replication to improve the production of bio-based chemicals.

The antifeedant and antifungal cryptic metabolites isolated from tobacco endophytes induced by host medium and coculture.

Four new cryptic metabolites including one fumagillol derivative (2), one cyclohexenone derivative (4), one 10-membered lactone (5), and one natural 4-epi-brefeldin C (8), along with seven known compounds were found from isogenesis endophytes Aspergillus fumigatus, Penicillium janthinellum, Nigrospora sp., and Stagonosporopsis sp. induced by host Nicotiana tabacum medium and co-culture. The structures were determined mainly by spectroscopic methods, including extensive 1D, 2D NMR, MS techniques, ECD calculation, and Mosher's method. Compound 2 possessed a novel 1, 3-dioxetane residue and cyclohexane-containing terpenoid skeleton. Compounds 2, 4-7 and 10 showed significant antifungal activities against the plant pathogen Nigrospora sp. with MICs of 1 µg/mL. 2, 4, 5-7, and 10 indicated antifungal activities against Penicillium janthinellum, Aspergillus fumigatus, Phomopsis sp., and Alternaria sp. with MICs ≤8 µg/mL. Compounds 2, 6-8, and 10 (50 µg/cm2) and microbial fermentation extracts (100 µg/cm2) showed antifeedant activities against silkworms with feeding deterrence indices of 21-100%.

Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms.

Background: In this paper, we examine whether machine learning and deep learning can be used to predict difficult airway intubation in patients undergoing thyroid surgery. Methods: We used 10 machine learning and deep learning algorithms to establish a corresponding model through a training group, and then verify the results in a test group. We used R for the statistical analysis and constructed the machine learning prediction model in Python. Results: The top 5 weighting factors for difficult airways identified by the average algorithm in machine learning were age, sex, weight, height, and BMI. In the training group, the AUC values and accuracy and the Gradient Boosting precision were 0.932, 0.929, and 100%, respectively. As for the modeled effects of predicting difficult airways in test groups, among the models constructed by the 10 algorithms, the three algorithms with the highest AUC values were Gradient Boosting, CNN, and LGBM, with values of 0.848, 0.836, and 0.812, respectively; In addition, among the algorithms, Gradient Boosting had the highest accuracy with a value of 0.913; Additionally, among the algorithms, the Gradient Boosting algorithm had the highest precision with a value of 100%. Conclusion: According to our results, Gradient Boosting performed best overall, with an AUC >0.8, an accuracy >90%, and a precision of 100%. Besides, the top 5 weighting factors identified by the average algorithm in machine learning for difficult airways were age, sex, weight, height, and BMI.

The production of broad-spectrum antibiotics from phytopathogen Epicoccum sorghinum by culturing in host edible mushroom Thelephora ganbajun extract.

Metabolism of special endophytes and phytopathogens can be induced by the symbiotic interactions with the host. A phytopathogen Epicoccum sorghinum cultured in host mushroom Thelephora ganbajun medium exhibited different metabolites compared with that of ordinary medium. An unprecedented scaffold possessing the same substructure as perylenequinone mycotoxin, a first methyl rearrangement product of phytotoxin, epoxydon 6-methylsalicylate ester, three undescribed compounds, and an undescribed natural product were isolated from E. sorghinum cultured in T. ganbajun. Episorin A and epicosorin A were produced from E. sorghinum induced by culturing in host medium. Episorin A was the first example of perylenequinone analogue in the natural products. These induced compounds and other metabolites showed notable antibiosis against endogenous fungi, and insect existing in mushroom. Induced episorin A showed significant inhibitory effects on nitric oxide production in LPS-activated macrophages, and anti-acetylcholinesterase with the IC50 at 5.40 ± 0.25 µM, and 4.32 µM, respectively, and cytotoxicity against HL-60, A-549, SMMC-7721, MCF-7 and SW480 with IC50 at 14.21 ± 0.53, 17.93 ± 0.22, 18.17 ± 0.63, 28.36 ± 0.43, and 18.20 ± 1.03 µM.

Prevalence of tobacco dependence and associated factors in China: Findings from nationwide China Health Literacy Survey during 2018-19.

Background: Tobacco dependence is the key barrier to successful smoking cessation. However, little is known about its prevalence, sociodemographic characteristics and determinants. We aimed to estimate the prevalence, associated factors and burden of tobacco dependence in China. Methods: During 2018-2019, the nationally representative 2018 China Health Literacy Survey (2018 CHLS) invited 87,708 participants to participate using a multistage stratified sampling method from 31 provinces (or equivalent) in mainland China, and 84,839 participants aged 20-69 with valid data were included in the analysis. We diagnosed tobacco dependence based on international criteria (ICD-10, DSM-4) and tailored to Chinese population according to China Clinical Guideline for Tobacco Cessation (2015 version). The prevalence of tobacco dependence was estimated overall and by sociodemographic factors. The Logistic regression was conducted to estimate odds ratios (OR) and 95% confidence intervals (CIs) for tobacco dependence and success of smoking cessation (being ex-smokers), with different levels of adjustment. These were used to estimate the total number of adults who were tobacco dependent in China. Findings: In China, the estimated prevalence of current smoking was 25.1%, significantly higher in men than in women (47.6% vs 1.9%). The prevalence of current smoking varied approximately 3-fold (12.9% to 37.9%) across 31 provinces of China. Among general population aged 20-69 years, the prevalence of tobacco dependence was 13.1% (95% CI:12.2-14.1). Among current smokers, the prevalence of tobacco dependence was 49.7% (46.5-52.9%), with no difference between men and women (49.7% vs 50.8%). The prevalence of tobacco dependence was associated significantly with smoking intensity, defined by pack-years (1.62 [1.54-1.70] per 10 pack-years), cigarettes smoked per day (2.01 [1.78, 2.27] per 10 cigarettes), and smoking starting age (0.93 [0.90, 0.97] per 5 years). Given smoking intensity, the prevalence of tobacco dependence also varied by age, gender, certain socioeconomic status and regions. Compared with those without tobacco dependence, ever smokers with tobacco dependence were less likely to be ex-smokers (2.88, 2.59-3.21). In China, 183.5 (170.4-197.4) million adults (177.5 million were men) were tobacco dependent in 2018. Interpretation: In China, tobacco dependence is highly prevalent, with approximately half of current smokers being addictive, highlighting the need for coordinated effort to improve awareness, diagnosis and treatment of tobacco dependence. Funding: Chinese Academy of Medical Sciences (CAMS) Initiative for Innovative Medicine (CAMS 2021-I2M-1-010), National Key R&D Program of China (grant no 2017YFC1309400), and National Natural Science Foundation of China (grant no 81720108001). Note: Chinese translation of abstract is available in appendix section.

Aliidiomarina halalkaliphila sp. nov., a haloalkaliphilic bacterium isolated from a soda lake in Inner Mongolia Autonomous Region, China.

A haloalkaliphilic strain (IM 1326T) was isolated from brine sampled at a soda lake in the Inner Mongolia Autonomous Region, China. Cells of the strain were rod-shaped and motile. Strain IM 1326T was able to grow at 4-42 °C (optimum, 37 °C) with 0-13.0 % (w/v) NaCl concentrations (optimum at 4.0-6.0 %) and at pH 7.5-11.0 (optimum at 9.0-10.0). The 16S rRNA gene phylogenetic analysis revealed that the isolate belongs to the genus Aliidiomarina and is closely related to the type strains of Aliidiomarina sanyensis (95.8 % sequence similarity), Aliidiomarina shirensis (95.7 %), Aliidiomarina iranensis (95.4 %) and Aliidiomarina haloalkalitolerans (95.3 %). The whole genome of strain IM 1326T was sequenced, and the genomic DNA G+C content was 49.7 mol%. Average nucleotide identity, average amino acid identity and digital DNA-DNA hybridization values between the isolate and the related Aliidiomarina species were 68.1-84.9 %, 76-78 % and 18.4-20.4 %, respectively. The respiratory quinone was ubiquinone-8. The polar lipid profile included diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine and one unidentified aminophospholipid. The predominant cellular fatty acids were summed feature 9 (10-methyl-C16 : 0/iso-C17 : 1 ω9c, 22.2 %), iso-C15 : 0 (16.1 %) and iso-C17 : 0 (13.1 %). Based on the results of phylogenetic analysis, genome relatedness, and the physiological and chemotaxonomic properties of the isolate, strain IM 1326T is considered to represent a novel species of the genus Aliidiomarina, for which the name Aliidiomarina halalkaliphila sp. nov. is proposed (type strain IM 1326T=CGMCC 1.17056T=JCM 34227T).

Geraniol relieves mycoplasma pneumonia infection-induced lung injury in mice through the regulation of ERK/JNK and NF-κB signaling pathways.

BACKGROUND: Pneumonia is a serious pediatric lung injury disease caused by Mycoplasma pneumoniae (M. pneumoniae) with increasing global prevalence every year. The WHO has reported that nearly 19% of children die due to pneumonia worldwide. OBJECTIVE: The present research was conducted to discover the ameliorative properties of geraniol against M. pneumoniae-provoked pneumonia in mice through the modulation of inflammatory responses. METHODOLOGY: The pneumonia was provoked in the male Swiss albino mice via infecting animals with 100 µl of M. pneumoniae for 2 days and supplemented concurrently with 20 mg/kg of geraniol for 3 days. 100 mg/kg of azithromycin was used as a standard drug. The nitric oxide (NO) level and MPO activity were measured using kits. The SOD activity, GSH, and MDA levels were studied using standard methods. The polymerase chain reaction (PCR) study was performed to examine the M. pneumoniae DNA load. The inflammatory cytokines status was assessed by assay kits. The ERK1/2, JNK1/2, and NF-κB expressions were studied by reverse-transcription (RT-PCR). The lung tissues were analyzed microscopically to investigate the histological alterations. RESULTS: Geraniol treatment effectively reduced lung weight, NO level, and MPO activity in the pneumonia mice. The total cells and M. pneumoniae DNA load were also decreased by the geraniol. The SOD activity and GSH level were improved and MDA was decreased by the geraniol treatment. The IL-1, IL-6, IL-8, TNF-α, and TGF status were appreciably depleted by the geraniol in the pneumonia mice. Geraniol also suppressed the ERK1/2 and NF-κB expressions in the lung tissues. Histological findings also suggest the therapeutic roles of geraniol against pneumonia in mice. CONCLUSION: In summary, our results proved the beneficial roles of geraniol against the M. pneumoniae-provoked pneumonia. Geraniol could be a hopeful therapeutic agent to treat pneumonia in the future.

The experience of tobacco withdrawal symptoms among current smokers and ex-smokers in the general population: Findings from nationwide China Health Literacy Survey during 2018-19.

Objective: To clarify the extent to which smokers in the general population experience tobacco withdrawal symptoms and whether such experience differs in those who continue to smoke and those who stopped smoking. Methods: We included relevant questions in the nationally-representative China Health Literacy Survey (CHLS) conducted in 2018-2019. Among 87,028 participants, there were 22,115 ever-smokers aged 20-69 years who provided information on their smoking history and their experience of tobacco withdrawal symptoms. Multivariate logistic regressions were conducted to explore the association between withdrawal symptoms and other variables. Results: Among ever-smokers, there were 19,643 (88.8%) current smokers and 2,472 (11.2%) ex-smokers. Among current smokers, 61.3% reported having tried to quit smoking in the past. Overall, 61.1% of current smokers reported experiencing withdrawal symptoms: 69.9% of those who tried to quit smoking in the past and 47.5% of those who did not. A lower proportion of ex-smokers experienced withdrawal symptoms (46.3%) and the difference remained significant after controlling for demographic characteristics (OR = 1.76, 95% CI 1.62-1.93, P < 0.001). The most commonly reported withdrawal symptoms in both current smokers and ex-smokers were craving, restlessness and anxiety. In the multivariable-adjusted analyses, those who experienced withdrawal symptoms when they tried to quit smoking (OR: 2.05, 95% CI: 1.86-2.27) were less likely to successfully quit. Conclusions: The clinical picture of the tobacco withdrawal syndrome is the same in current smokers and in ex-smokers, but ex-smokers are less likely to have experienced it. The experience of discomfort when unable to smoke is common and seems likely to be a major factor contributing to maintaining smoking behavior not just among individuals seeking help with quitting smoking, but among smokers generally.

Nanomedicines in oral cancer: inspiration comes from extracellular vesicles and biomimetic nanoparticles.

Oral cancer is a common life-threatening malignancy having high mortality and morbidity rates. During the treatment process, individuals unavoidably experience severe side effects. It is essential to develop safer and more effective strategies. Currently, extracellular vesicles (EVs) and biomimetic nanoparticles are nanomedicines with long-term blood circulation and lower off-target toxicity that orchestrate immune responses and accumulate specifically in tumor sites. EVs create a synergetic effect by encapsulating drugs and collaborating with naturally loaded elements in the EVs. Biomimetic nanoparticles retain the characteristic features of the synthetic nanocarriers and inherit the intrinsic cell membrane functionalities. This review outlines the properties, applications, challenges, pros and cons of EVs and biomimetic nanoparticles, providing novel perspectives on oral cancer.

This review explains how extracellular vesicles (EVs) and biomimetic nanoparticles are emerging as nanomedicines applied in oral cancer. EVs are phospholipid bilayer vesicles, mainly including exosomes and microvesicles, responsible for intercellular communication and cargo transport. EVs can carry RNA, metabolites and other molecular payloads. Biomimetic nanomedicines are synthetic nanoparticles coated with the parent or host cell membrane to escape the immune system and elevate targeting ability. Various cell membranes have been used for camouflaging nanoparticles, such as red blood cells, white blood cells, platelets, mesenchymal stem cells and cancer cell membranes. During the treatment process, individuals unavoidably experience severe side effects. It is essential to develop safer and more effective strategies. Currently, EVs and biomimetic nanoparticles are nanomedicines with long-term blood circulation and lower off-target toxicity that orchestrate immune responses and accumulate specifically in tumor sites. EVs create a synergetic effect by encapsulating drugs and collaborating with naturally loaded elements in the EVs. Biomimetic nanoparticles retain the characteristic features of the synthetic nanocarriers and inherit the intrinsic cell membrane functionalities. This review outlines the properties, applications, challenges, pros and cons of EVs and biomimetic nanoparticles, providing novel perspectives on oral cancer.

Corrigendum: TRPC1 Inhibits Cell Proliferation/Invasion and Is Predictive of a Better Prognosis of Esophageal Squamous Cell Carcinoma.

[This corrects the article DOI: 10.3389/fonc.2021.627713.].

The selective anti-fungal metabolites from Irpex lacteus and applications in the chemical interaction of Gastrodia elata, Armillaria sp., and endophytes.

The investigation of the metabolites from endophyte Irpex lacteus cultured in host "tian ma" (Gastrodia elata) revealed five new tremulane sesquiterpenes (1-5), and a new tetrahydrofuran derivative (6). Compound 1 was the first tremulane glucoside, and 6 possessed a rare tetrahydropyran-tetrahydrofuran scaffold. Main metabolite (2,3-dihydroxydodacane-4,7-dione, 14) from I. lacteus showed significant selectivity for antifungal activity against phytopathogen and endophytes associated with G. elata rather than against Armillaria sp. providing nutrition for the host G. elata. 14 accounted for 27.4% of isolated compounds from G. elata medium, and 69.3% by co-culturing with Armillaria sp. So the I. lacteus tended to promote the growth of Armillaria sp. in co-culture by producing 2,3-dihydroxydodacane-4,7-dione (14) to selective inhibit the phytopathogen and endophyte existed in host G. elata for the benefit of G. elata-Armillaria symbiosis. And the results were in accord with the real environment of G. elata depending on the nutrition of Armillaria. Some metabolites had anti-fungal activities against phytopathogens of G. elata with MICs ≤8 µg/mL.

Adrenomedullin: new inhibitory regulator for cortisol synthesis and secretion.

Preterm birth is associated with immaturity of several crucial physiological functions notably those prevailing in the lung and kidney. Recently, a steroid secretion deficiency was identified in very preterm neonates, associated with a partial yet transient deficiency in 11ß-hydroxylase activity, sustaining cortisol synthesis. However, the P450c11ß enzyme is expressed in preterm adrenal glands, we hypothesized an inhibition of cortisol production by adrenomedullin (ADM), a peptide highly produced in neonates and whose effect on steroidogenesis remains poorly known. We studied the effects of ADM on three models: 104 cord-blood samples of the PREMALDO neonate cohort, genetically targeted mice overexpressing ADM, and two human adrenocortical cell lines (H295R and HAC15 cells). Mid-regional-proADM (MR-proADM) quantification in cord-blood samples showed strong negative correlation with gestational age (P = 0.0004), cortisol production (P < 0.0001), and 11ß-hydroxylase activity index (P < 0.0001). Mean MR-proADM was higher in very preterm than in term neonates (1.12 vs 0.60 nmol/L, P < 0.0001). ADM-overexpression mice revealed a lower 11ß-hydroxylase activity index (P < 0.05). Otherwise, aldosterone levels measured by LC-MS/MS were higher in ADM-overexpression mice (0.83 vs 0.46 ng/mL, P < 0.05). More importantly, the negative relationship between adrenal ADM expression and aldosterone production found in control was lacking in the ADM-overexpression mice. Finally, LC-MS/MS and gene expression studies on H295R and HAC15 cells revealed an ADM-induced inhibition of both cortisol secretion in cell supernatants and CYP11B1 expression. Collectively, our results converge toward an inhibitory effect of ADM on glucocorticoid synthesis in humans and should be considered to explain the steroid secretion deficiency observed at birth in premature newborns.

Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery.

Objective: Investigate whether machine learning can predict pulmonary complications (PPCs) after emergency gastrointestinal surgery in patients with acute diffuse peritonitis. Methods: This is a secondary data analysis study. We use five machine learning algorithms (Logistic regression, DecisionTree, GradientBoosting, Xgbc, and gbm) to predict postoperative pulmonary complications. Results: Nine hundred and twenty-six cases were included in this study; 187 cases (20.19%) had PPCs. The five most important variables for the postoperative weight were preoperative albumin, cholesterol on the 3rd day after surgery, albumin on the day of surgery, platelet count on the 1st day after surgery and cholesterol count on the 1st day after surgery for pulmonary complications. In the test group: the logistic regression model shows AUC = 0.808, accuracy = 0.824 and precision = 0.621; Decision tree shows AUC = 0.702, accuracy = 0.795 and precision = 0.486; The GradientBoosting model shows AUC = 0.788, accuracy = 0.827 and precision = 1.000; The Xgbc model shows AUC = 0.784, accuracy = 0.806 and precision = 0.583. The Gbm model shows AUC = 0.814, accuracy = 0.806 and precision = 0.750. Conclusion: Machine learning algorithms can predict patients' PPCs with acute diffuse peritonitis. Moreover, the results of the importance matrix for the Gbdt algorithm model show that albumin, cholesterol, age, and platelets are the main variables that account for the highest pulmonary complication weights.