Exhaled acetone and isoprene in perioperative lung cancer patients under intensive oral care: possible indicators of inflammatory responses and metabolic changes

SARS CoV-2 infection screening via the exhaled breath fingerprint obtained by FTIR spectroscopic gas-phase analysis. A proof of concept

The COVID-19 pandemic remains a global challenge now with the long-COVID arising. Mitigation measures focused on case counting, assessment and determination of variants and their likely targets of infection and transmission, the pursuit of drug treatments, use and enhancement of masks, social distancing, vaccination, post-infection rehabilitation, and mass screening. The latter is of utmost importance given the current scenario of infections, reinfections, and long-term health effects. Research on screening platforms has been developed to provide more sensitive, specific, and reliable tests that are accessible to the entire population and can be used to assess the prognosis of the disease as well as the subsequent health follow-up of patients with sequelae of COVID-19. Therefore, the aim of the present study was the simulation of exhaled breath of COVID-19 patients by evaluation of three identified COVID-19 indicator breath biomarkers (acetone (ACE), acetaldehyde (ACH) and nitric oxide (NO)) by gas-phase infrared spectroscopy as a proof-of-concept principle for the detection of infected patients' exhaled breath fingerprint and subsequent follow-up. The specific fingerprints of each of the compounds and the overall fingerprint were obtained. The synthetic exhaled breath evaluation concept revealed a linearity of r = 0.99 for all compounds, and LODs of 6.42, 13.81, 9.22 ppm, and LOQs of 42.26, 52.57, 69.23 ppm for NO, ACE, and ACH, respectively. This study proves the fundamental feasibility of gas-phase infrared spectroscopy for fingerprinting lung damage biomarkers in exhaled breath of patients with COVID-19. This analysis would allow faster and cheaper screening and follow-up of infected individuals, which could improve mass screening in POC settings.

Biomarkers of mitochondrial dysfunction in acute respiratory distress syndrome: A systematic review and meta-analysis

Introduction

Acute respiratory distress syndrome (ARDS) is one of the main causes of Intensive Care Unit morbidity and mortality. Metabolic biomarkers of mitochondrial dysfunction are correlated with disease development and high mortality in many respiratory conditions, however it is not known if they can be used to assess risk of mortality in patients with ARDS.

Objectives

The aim of this systematic review was to examine the link between recorded biomarkers of mitochondrial dysfunction in ARDS and mortality.

Methods

A systematic review of CINAHL, EMBASE, MEDLINE, and Cochrane databases was performed. Studies had to include critically ill ARDS patients with reported biomarkers of mitochondrial dysfunction and mortality. Information on the levels of biomarkers reflective of energy metabolism and mitochondrial respiratory function, mitochondrial metabolites, coenzymes, and mitochondrial deoxyribonucleic acid (mtDNA) copy number was recorded. RevMan5.4 was used for meta-analysis. Biomarkers measured in the samples representative of systemic circulation were analyzed separately from the biomarkers measured in the samples representative of lung compartment. Cochrane risk of bias tool and Newcastle-Ottawa scale were used to evaluate publication bias (Prospero protocol: CRD42022288262).

Results

Twenty-five studies were included in the systematic review and nine had raw data available for follow up meta-analysis. Biomarkers of mitochondrial dysfunction included mtDNA, glutathione coupled mediators, lactate, malondialdehyde, mitochondrial genetic defects, oxidative stress associated markers. Biomarkers that were eligible for meta-analysis inclusion were: xanthine, hypoxanthine, acetone, N-pentane, isoprene and mtDNA. Levels of mitochondrial biomarkers were significantly higher in ARDS than in non-ARDS controls (P = 0.0008) in the blood-based samples, whereas in the BAL the difference did not reach statistical significance (P = 0.14). mtDNA was the most frequently measured biomarker, its levels in the blood-based samples were significantly higher in ARDS compared to non-ARDS controls (P = 0.04). Difference between mtDNA levels in ARDS non-survivors compared to ARDS survivors did not reach statistical significance (P = 0.05).

Conclusion

Increased levels of biomarkers of mitochondrial dysfunction in the blood-based samples are positively associated with ARDS. Circulating mtDNA is the most frequently measured biomarker of mitochondrial dysfunction, with significantly elevated levels in ARDS patients compared to non-ARDS controls. Its potential to predict risk of ARDS mortality requires further investigation.

Systematic review registration

[https://www.crd.york.ac.uk/prospero], identifier [CRD42022288262].

Artificial Neural Network Assisted Cancer Risk Prediction of Oral Precancerous Lesions

The incidence of oral cancer is still increasing. It has become very common in patients with malignant tumors, which has forced medical personnel to continuously explore its treatment methods. What kind of method can effectively and correctly diagnose the disease in the early stage and improve the survival rate has become one of the research topics that have attracted much attention. Aiming at this problem, it has great research significance for the field of oral precancerous lesions diagnosis. With the in-depth research on oral precancerous diagnosis, the research on artificial neural network (ANN) in medical diagnosis is gradually carried out. Its performance advantage is of great significance to solve the problem of early and correct disease diagnosis. This paper aimed to investigate the application of ANN-assisted cancer risk prediction method in risk prediction of oral precancerous lesions. Through the analysis and research of ANN and oral cancer, the construction of oral cancer risk prediction model was applied to solve the problem of improving the survival rate of oral cancer patients. In this paper, ANN and oral precancerous lesions were analyzed, the performance of the algorithm was experimentally analyzed, and the relevant theoretical formulas were used to explain. The results showed that the method had higher accuracy than traditional forecasting methods. When $N=2$ , the output accuracy was above 90%. It can be seen that the algorithm can meet the needs of the diagnosis of high-risk groups of oral cancer lesions, and the diagnosis efficiency and patient survival rate has been greatly improved.

Relationship between Oral Health Status and Postoperative Fever among Patients with Lung Cancer Treated by Surgery: A Retrospective Cohort Study

A retrospective observational study using an oral bacteria counter was conducted to evaluate the trends in the number of oral bacteria in the perioperative period of lung cancer patients and to verify the relationship between oral health status and postoperative fever. All patients received perioperative oral management (POM) by oral specialists between April 2012 and December 2018 at Kagawa Prefectural Central Hospital, Kagawa, Japan prior to lung cancer surgery. Bacteria counts from the dorsum of the tongue were measured on the day of pre-hospitalization, pre-operation, and post-operation, and background data were also collected retrospectively. In total, 441 consecutive patients were enrolled in the study. Bonferroni’s multiple comparison test showed significantly higher oral bacteria counts at pre-hospitalization compared to pre- and post-operation (p < 0.001). Logistic regression analysis showed that body mass index, performance status, number of housemates, number of teeth, and white blood cell count at pre-operation were significantly associated with postoperative fever. The study showed that POM can reduce the level of oral bacterial counts, that the risk of postoperative complications is lower with dentulous patients, and that appropriate POM is essential for prevent of complications. Therefore, POM may play an important role in perioperative management of lung cancer patients.

Pneumonia prevention effects of perioperative oral management in approximately 25,000 patients following cancer surgery

AIM

We conducted a multicenter study to explore the risk factors of developing pneumonia and the effectiveness of perioperative oral management (POM) for the prevention of pneumonia in postsurgical patients.

METHODS AND RESULTS

A survey covering eight regional hospitals was conducted over 4 years, from April 2010 to March 2014. Using the Diagnosis Procedure Combination database, a target group of 25,554 patients with cancer who underwent surgery was selected and assessed from a population of 346,563 patients without pneumonia on admission (sample population). The study compared the incidence of pneumonia and attempted to identify the significant predictive factors for its occurrence in these patients using multiple logistic regression analysis. Comparative assessment for the occurrence of pneumonia before and after POM implementation showed a significant incidence decrease after POM introduction in the target group, with no such change observed in the sample population. Multiple logistic regression analysis showed that the odds ratio for pneumonia occurrence after POM introduction was 0.44, indicating a reduced risk of pneumonia.

CONCLUSION

POM in cancer patients was indeed effective in reducing the incidence of pneumonia in hospitals and thereby helped in preventing pneumonia during hospitalization.