Generative AI in Medical Imaging: Applications, Challenges, and Ethics.

Medical imaging is playing an important role in diagnosis and treatment of diseases. Generative artificial intelligence (AI) have shown great potential in enhancing medical imaging tasks such as data augmentation, image synthesis, image-to-image translation, and radiology report generation. This commentary aims to provide an overview of generative AI in medical imaging, discussing applications, challenges, and ethical considerations, while highlighting future research directions in this rapidly evolving field.

Anticoagulant therapy in COVID-19: A narrative review.

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can manifest itself in several ways, including coagulopathy and thrombosis. These complications can be the first and sometimes only manifestations of SARS-CoV-2 infection and can occur early or late in the course of the disease. However, these symptoms are more prevalent in hospitalized patients with venous thromboembolism, particularly those admitted to intensive care units. Moreover, various forms of arterial and venous thrombosis, or micro- or macro-vasculature embolisms, have been reported during the current pandemic. They have led to harmful consequences, such as neurological and cardiac events, nearly all resulting from the hypercoagulable state caused by this viral infection. The severe hypercoagulability observed in patients with COVID-19 accounts for most cases of the disease that become critical. Therefore, anticoagulants seem to be one of the most vital therapeutics for treating this potentially life-threatening condition. In the current paper, we present a thorough review of the pathophysiology of COVID-19-induced hypercoagulable state and the use of anticoagulants to treat SARS-CoV-2 infections in different patient groups, as well as their pros and cons.

A review of the potential neurological adverse events of COVID-19 vaccines.

Despite the advantages of getting access to the coronavirus disease 2019 (COVID-19) vaccines, their potential ability to induce severe adverse events (AEs) has been a significant concern. Neurological complications are significant among the various adverse events following immunization (AEFI) due to their likely durability and debilitating sequelae. Neurological AEs following COVID-19 vaccination can either exacerbate or induce new-onset neuro-immunologic diseases, such as myasthenia gravis (MG) and Guillain-Barre syndrome (GBS). The more severe spectrum of AEs post-COVID19 vaccines has included seizures, reactivation of the varicella-zoster virus, strokes, GBS, Bell's palsy, transverse myelitis (TM), and acute disseminated encephalomyelitis (ADEM). Here, we discuss each of these neurological adverse effects separately.

A Multimodal Deep Learning Approach to Predicting Systemic Diseases from Oral Conditions.

Background: It is known that oral diseases such as periodontal (gum) disease are closely linked to various systemic diseases and disorders. Deep learning advances have the potential to make major contributions to healthcare, particularly in the domains that rely on medical imaging. Incorporating non-imaging information based on clinical and laboratory data may allow clinicians to make more comprehensive and accurate decisions. Methods: Here, we developed a multimodal deep learning method to predict systemic diseases and disorders from oral health conditions. A dual-loss autoencoder was used in the first phase to extract periodontal disease-related features from 1188 panoramic radiographs. Then, in the second phase, we fused the image features with the demographic data and clinical information taken from electronic health records (EHR) to predict systemic diseases. We used receiver operation characteristics (ROC) and accuracy to evaluate our model. The model was further validated by an unseen test dataset. Findings: According to our findings, the top three most accurately predicted chapters, in order, are the Chapters III, VI and IX. The results indicated that the proposed model could predict systemic diseases belonging to Chapters III, VI and IX, with AUC values of 0.92 (95% CI, 0.90-94), 0.87 (95% CI, 0.84-89) and 0.78 (95% CI, 0.75-81), respectively. To assess the robustness of the models, we performed the evaluation on the unseen test dataset for these chapters and the results showed an accuracy of 0.88, 0.82 and 0.72 for Chapters III, VI and IX, respectively. Interpretation: The present study shows that the combination of panoramic radiograph and clinical oral features could be considered to train a fusion deep learning model for predicting systemic diseases and disorders.

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.

Background: The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs. Methods: PubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study. Results: ML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy. Conclusion: Overall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designs. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.

Captopril Alleviates Chondrocyte Senescence in DOCA-Salt Hypertensive Rats Associated with Gut Microbiome Alteration.

Gut microbiota is the key controller of healthy aging. Hypertension and osteoarthritis (OA) are two frequently co-existing age-related pathologies in older adults. Both are associated with gut microbiota dysbiosis. Hereby, we explore gut microbiome alteration in the Deoxycorticosterone acetate (DOCA)-induced hypertensive rat model. Captopril, an anti-hypertensive medicine, was chosen to attenuate joint damage. Knee joints were harvested for radiological and histological examination; meanwhile, fecal samples were collected for 16S rRNA and shotgun sequencing. The 16S rRNA data was annotated using Qiime 2 v2019.10, while metagenomic data was functionally profiled with HUMAnN 2.0 database. Differential abundance analyses were adopted to identify the significant bacterial genera and pathways from the gut microbiota. DOCA-induced hypertension induced p16INK4a+ senescent cells (SnCs) accumulation not only in the aorta and kidney (p < 0.05) but also knee joint, which contributed to articular cartilage degradation and subchondral bone disturbance. Captopril removed the p16INK4a + SnCs from different organs, partially lowered blood pressure, and mitigated cartilage damage. Meanwhile, these alterations were found to associate with the reduction of Escherichia-Shigella levels in the gut microbiome. As such, gut microbiota dysbiosis might emerge as a metabolic link in chondrocyte senescence induced by DOCA-triggered hypertension. The underlying molecular mechanism warrants further investigation.

Early stage NSCLS patients' prognostic prediction with multi-information using transformer and graph neural network model.

Background: We proposed a population graph with Transformer-generated and clinical features for the purpose of predicting overall survival (OS) and recurrence-free survival (RFS) for patients with early stage non-small cell lung carcinomas and to compare this model with traditional models. Methods: The study included 1705 patients with lung cancer (stages I and II), and a public data set for external validation (n=127). We proposed a graph with edges representing non-imaging patient characteristics and nodes representing imaging tumour region characteristics generated by a pretrained Vision Transformer. The model was compared with a TNM model and a ResNet-Graph model. To evaluate the models' performance, the area under the receiver operator characteristic curve (ROC-AUC) was calculated for both OS and RFS prediction. The Kaplan-Meier method was used to generate prognostic and survival estimates for low- and high-risk groups, along with net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis. An additional subanalysis was conducted to examine the relationship between clinical data and imaging features associated with risk prediction. Results: Our model achieved AUC values of 0.785 (95% confidence interval [CI]: 0.716-0.855) and 0.695 (95% CI: 0.603-0.787) on the testing and external data sets for OS prediction, and 0.726 (95% CI: 0.653-0.800) and 0.700 (95% CI: 0.615-0.785) for RFS prediction. Additional survival analyses indicated that our model outperformed the present TNM and ResNet-Graph models in terms of net benefit for survival prediction. Conclusions: Our Transformer-Graph model was effective at predicting survival in patients with early stage lung cancer, which was constructed using both imaging and non-imaging clinical features. Some high-risk patients were distinguishable by using a similarity score function defined by non-imaging characteristics such as age, gender, histology type, and tumour location, while Transformer-generated features demonstrated additional benefits for patients whose non-imaging characteristics were non-discriminatory for survival outcomes. Funding: The study was supported by the National Natural Science Foundation of China (91959126, 8210071009), and Science and Technology Commission of Shanghai Municipality (20XD1403000, 21YF1438200).

Study of the frequency and clinical features of maturity-onset diabetes in the young in the pediatric and adolescent diabetes population in Iran.

OBJECTIVES: Maturity-onset diabetes of the young (MODY), an autosomal dominant disease, is frequently misdiagnosed as type 1 or 2 diabetes. Molecular diagnosis is essential to distinguish them. This study was done to investigate the prevalence of MODY subtypes and patients' clinical characteristics. METHODS: A total of 43 out of 230 individuals with diabetes were selected based on the age of diagnosis >6 months, family history of diabetes, absence of marked obesity, and measurable C-peptide. Next-generation and direct SANGER sequencing was performed to screen MODY-related mutations. The variants were interpreted using the Genome Aggregation Database (genomAD), Clinical Variation (ClinVar), and pathogenicity prediction tools. RESULTS: There were 23 males (53.5%), and the mean age at diabetes diagnosis was 6.7 ± 3.6 years. Sixteen heterozygote single nucleotide variations (SNVs) from 14 patients (14/230, 6%) were detected, frequently GCK (37.5%) and BLK (18.7%). Two novel variants were identified in HNF4A and ABCC8. Half of the detected variants were categorized as likely pathogenic. Most prediction tools predicted Ser28Cys in HNF4A as benign and Tyr123Phe in ABCC8 as a pathogenic SNV. Six cases (42.8%) with positive MODY SNVs had islet autoantibodies. At diagnosis, age, HbA1c, and C-peptide level were similar between SNV-positive and negative patients. CONCLUSIONS: This is the first study investigating 14 variants of MODY in Iran. The results recommend genetic screening for MODY in individuals with unusual type 1 or 2 diabetes even without family history. Treatment modifies depending on the type of patients' MODY and is associated with the quality of life.

Self-supervised region-aware segmentation of COVID-19 CT images using 3D GAN and contrastive learning.

Medical image segmentation is a key initial step in several therapeutic applications. While most of the automatic segmentation models are supervised, which require a well-annotated paired dataset, we introduce a novel annotation-free pipeline to perform segmentation of COVID-19 CT images. Our pipeline consists of three main subtasks: automatically generating a 3D pseudo-mask in self-supervised mode using a generative adversarial network (GAN), leveraging the quality of the pseudo-mask, and building a multi-objective segmentation model to predict lesions. Our proposed 3D GAN architecture removes infected regions from COVID-19 images and generates synthesized healthy images while keeping the 3D structure of the lung the same. Then, a 3D pseudo-mask is generated by subtracting the synthesized healthy images from the original COVID-19 CT images. We enhanced pseudo-masks using a contrastive learning approach to build a region-aware segmentation model to focus more on the infected area. The final segmentation model can be used to predict lesions in COVID-19 CT images without any manual annotation at the pixel level. We show that our approach outperforms the existing state-of-the-art unsupervised and weakly-supervised segmentation techniques on three datasets by a reasonable margin. Specifically, our method improves the segmentation results for the CT images with low infection by increasing sensitivity by 20% and the dice score up to 4%. The proposed pipeline overcomes some of the major limitations of existing unsupervised segmentation approaches and opens up a novel horizon for different applications of medical image segmentation.

Artificial intelligence-designed single molar dental prostheses: A protocol of prospective experimental study.

BACKGROUND: Dental prostheses, which aim to replace missing teeth and to restore patients' appearance and oral functions, should be biomimetic and thus adopt the occlusal morphology and three-dimensional (3D) position of healthy natural teeth. Since the teeth of an individual subject are controlled by the same set of genes (genotype) and are exposed to mostly identical oral environment (phenotype), the occlusal morphology and 3D position of teeth of an individual patient are inter-related. It is hypothesized that artificial intelligence (AI) can automate the design of single-tooth dental prostheses after learning the features of the remaining dentition. MATERIALS AND METHODS: This article describes the protocol of a prospective experimental study, which aims to train and to validate the AI system for design of single molar dental prostheses. Maxillary and mandibular dentate teeth models will be collected and digitized from at least 250 volunteers. The (original) digitized maxillary teeth models will be duplicated and processed by removal of right maxillary first molars (FDI tooth 16). Teeth models will be randomly divided into training and validation sets. At least 200 training sets of the original and the processed digitalized teeth models will be input into 3D Generative Adversarial Network (GAN) for training. Among the validation sets, tooth 16 will be generated by AI on 50 processed models and the morphology and 3D position of AI-generated tooth will be compared to that of the natural tooth in the original maxillary teeth model. The use of different GAN algorithms and the need of antagonist mandibular teeth model will be investigated. Results will be reported following the CONSORT-AI.

Effect of an Endoprosthetic Reconstruction Tube and Medial Gastrocnemius Flap on Extensor Mechanism Function After Oncologic Prosthesis Reconstruction of the Proximal Tibia.

BACKGROUND: Extensor mechanism reconstruction after the proximal tibial resection and implantation of a megaprosthesis is challenging. In this study, we evaluated the effectiveness of the Trevira tube and medial gastrocnemius flap in restoring extensor mechanism following the resection of proximal tibial tumor and implantation of megaprosthesis. METHODS: Forty patients who underwent endoprosthetic implantation following the resection of proximal tibial tumor and patellar tendon reconstruction with the Trevira tube and medial gastrocnemius flap were included. The outcome measures were knee range of motion, extensor mechanism function, patellar position, and limb function subjectively evaluated through Toronto Extremity Salvage Score and objectively through Musculoskeletal Tumor Society score. The mean follow-up of the patients was 6.1 years. RESULTS: The patellar position was normal in 28 (70%) patients, patella baja in 3 (7.5%) patients, and patella alta in 9 (22.5%) patients. The mean active knee range of motion was 98.9 ± 17° (range: 85°-125°). Extension lag was present in 7 (17.5%) patients (range: 5°-20°). The mean Toronto Extremity Salvage Score of patients was 92.1 ± 6.9% (range: 85-100). The mean Musculoskeletal Tumor Society score of the patients was 87.7 ± 13 (range: 73.3-100). Postoperative complications included aseptic wound dehiscence (2 patients), aseptic loosening of the tibial component (1 patient), periprosthetic fracture in the femur (2 patients), and wound infection (1 patient). CONCLUSION: Trevira tube combined with gastrocnemius flap augmentation is a suitable procedure for restoring extensor mechanism after proximal tibial resection and megaprosthesis implantation.

Re-sensitization of mcr carrying multidrug resistant bacteria to colistin by silver.

Colistin is considered the last-line antimicrobial for the treatment of multidrug-resistant gram-negative bacterial infections. The emergence and spread of superbugs carrying the mobile colistin resistance gene (mcr) have become the most serious and urgent threat to healthcare. Here, we discover that silver (Ag+), including silver nanoparticles, could restore colistin efficacy against mcr-positive bacteria. We show that Ag+ inhibits the activity of the MCR-1 enzyme via substitution of Zn2+ in the active site. Unexpectedly, a tetra-silver center was found in the active-site pocket of MCR-1 as revealed by the X-ray structure of the Ag-bound MCR-1, resulting in the prevention of substrate binding. Moreover, Ag+effectively slows down the development of higher-level resistance and reduces mutation frequency. Importantly, the combined use of Ag+ at a low concentration with colistin could relieve dermonecrotic lesions and reduce the bacterial load of mice infected with mcr-1­carrying pathogens. This study depicts a mechanism of Ag+ inhibition of MCR enzymes and demonstrates the potentials of Ag+ as broad-spectrum inhibitors for the treatment of mcr-positive bacterial infection in combination with colistin.

Oncogenic and tumor suppressor genes expression in myeloproliferative neoplasms: The hidden side of a complex pathology.

BACKGROUND: The present study aimed to explore the changes in the expressions of six tumor-related genes in myeloproliferative neoplasms (MPNs). The study population included 130 patients with MPNs (52 with chronic myeloid leukemia (CML), 49 with essential thrombocythemia (ET), 20 with polycythemia vera (PV), and 9 with primary myelofibrosis (PMF)) and 51 healthy individuals. METHODS: The expression profiling of six genes (ADAMTS18, CMTM5, CDKN2B, DCC, FHIT, and WNT5B) in the peripheral blood granulocyte cells was explored by real-time quantitative reverse transcription polymerase chain reaction. RESULTS: The patients with MPNs showed significant downregulation of CMTM5 (EFC = 0.66) and DCC (EFC = 0.65) genes in contrast to a non-significant upregulation of ADAMTS18, CDKN2B, FHIT, and WNT5B genes. Downregulation of DCC was consistent in all subtypes of MPN (EFC range: 0.591-0.860). However, CMTM5 had a 1.22-fold upregulation in PMF in contrast to downregulation in other MPN subtypes (EFC range: 0.599-0.775). The results revealed a significant downregulation in CMTM5 and DCC at below 60-years of age. Furthermore, female patients showed a clear-cut downregulation in both CMTM5 and DCC (EFC DCC: 0.436 and CMTM5: 0.570), while male patients presented a less prominent downregulation with a borderline p-value only in DCC (EFC: 0.69; p = 0.05). CONCLUSIONS: Chronic myeloid leukemia cases showed a significant upregulation of WNT5B, as a known oncogenesis gene. Two tumor suppressor genes, namely DCC and CMTM5, were downregulated in the patients with MPNs, especially in females and patients below 60 years of age.

The prognostic significance of immune checkpoint receptor expression in patients with lymphoma: Association with disease status and clinical outcomes.

INTRODUCTION: Little is known about the expression of immune checkpoint receptors in the peripheral blood of lymphoma patients. Herein, we assessed the expression of inhibitory checkpoint receptors, including CTLA-4, PD-1/PDL-1, LAG-3, and TIM-3 in the peripheral blood of lymphoma patients and its correlation with the clinical outcomes of patients. Therefore, 47 classical Hodgkin lymphoma (cHL), 48 non-Hodgkin lymphoma patients with diffuse large B-cell lymphoma (DLBCL) subtype, and 30 healthy controls were recruited. METHODS: The expression of inhibitory receptors was evaluated using SYBR Green real-time PCR method. RESULTS: CTLA-4, LAG-3, and TIM-3 genes were significantly upregulated in both cHL and DLBCL patients compared to the healthy controls. In addition, the level of these molecules was differentially expressed in cHL and DLBCL patients at different disease phases compared to the healthy controls. The CTLA-4 gene was highly expressed in newly diagnosed (ND) cHL patients compared to the relapsed ones. Relapsed DLBCL patients had significantly increased LAG-3 expression compared to patients at remission, as well as ND patients. Regarding cHL patients, high CTLA-4 expression was correlated with low lactate dehydrogenase level and better performance status, whereas the level of LAG-3 was significantly elevated in patients with poor performance status. Lower initial PD-1 expression was associated with improved disease-free survival in cHL patients. CONCLUSIONS: Inhibitory immune checkpoint receptors are aberrantly expressed in the peripheral blood of cHL and DLBCL patients in which high LAG-3 in DLBCL patients and PD-1/LAG-3 in cHL patients are associated with relapse occurrence and worse prognosis, respectively.

Comparison of time-to-event machine learning models in predicting oral cavity cancer prognosis.

BACKGROUND: Applying machine learning to predicting oral cavity cancer prognosis is important in selecting candidates for aggressive treatment following diagnosis. However, models proposed so far have only considered cancer survival as discrete rather than dynamic outcomes. OBJECTIVES: To compare the model performance of different machine learning-based algorithms that incorporate time-to-event data. These algorithms included DeepSurv, DeepHit, neural net-extended time-dependent cox model (Cox-Time), and random survival forest (RSF). MATERIALS AND METHODS: Retrospective cohort of 313 oral cavity cancer patients were obtained from electronic health records. Models were trained on patient data following preprocessing. Predictors were based on demographic, clinicopathologic, and treatment information of the cases. Outcomes were the disease-specific and overall survival. Multivariable analyses were conducted to select significant prognostic features associated with tumor prognosis. Two models were generated per algorithm based on all-prognostic features and significant-prognostic features following statistical analysis. Concordance index (c-index) and integrated Brier scores were used as performance evaluators and model stability was assessed using intraclass correlation coefficients (ICC) calculated from these measures obtained from the cross-validation folds. RESULTS: While all models were satisfactory, better discriminatory performance and calibration was observed for disease-specific than overall survival (mean c-index: 0.85 vs 0.74; mean integrated Brier score: 0.12 vs 0.17). DeepSurv performed best in terms of discrimination for both outcomes (c-indices: 0.76 -0.89) while RSF produced better calibrated survival estimates (integrated Brier score: 0.06 -0.09). Model stability of the algorithms varied with the outcomes as Cox-Time had the best intraclass correlation coefficient (mean ICC: 1.00) for disease-specific survival while DeepSurv was most stable for overall survival prediction (mean ICC: 0.99). CONCLUSIONS: Machine learning algorithms based on time-to-event outcomes are successful in predicting oral cavity cancer prognosis with DeepSurv and RSF producing the best discriminative performance and calibration.

Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders.

Machine-intelligence platforms for the prediction of the probability of malignant transformation of oral potentially malignant disorders are required as adjunctive decision-making platforms in contemporary clinical practice. This study utilized time-to-event learning models to predict malignant transformation in oral leukoplakia and oral lichenoid lesions. A total of 1098 patients with oral white lesions from two institutions were included in this study. In all, 26 features available from electronic health records were used to train four learning algorithms-Cox-Time, DeepHit, DeepSurv, random survival forest (RSF)-and one standard statistical method-Cox proportional hazards model. Discriminatory performance, calibration of survival estimates, and model stability were assessed using a concordance index (c-index), integrated Brier score (IBS), and standard deviation of the averaged c-index and IBS following training cross-validation. This study found that DeepSurv (c-index: 0.95, IBS: 0.04) and RSF (c-index: 0.91, IBS: 0.03) were the two outperforming models based on discrimination and calibration following internal validation. However, DeepSurv was more stable than RSF upon cross-validation. External validation confirmed the utility of DeepSurv for discrimination (c-index-0.82 vs. 0.73) and RSF for individual survival estimates (0.18 vs. 0.03). We deployed the DeepSurv model to encourage incipient application in clinical practice. Overall, time-to-event models are successful in predicting the malignant transformation of oral leukoplakia and oral lichenoid lesions.

Arsenic trioxide targets Hsp60, triggering degradation of p53 and survivin.

The mechanisms of action of arsenic trioxide (ATO), a clinically used drug for the treatment of acute promyelocytic leukemia (APL), have been actively studied mainly through characterization of individual putative protein targets. There appear to be no studies at a system level. Herein, we integrate metalloproteomics through a newly developed organoarsenic probe, As-AC (C20H17AsN4O3S2) with quantitative proteomics, allowing 37 arsenic binding and 250 arsenic regulated proteins to be identified in NB4, a human APL cell line. Bioinformatics analysis reveals that ATO disrupts multiple physiological processes, in particular, chaperone-related protein folding and cellular response to stress. Furthermore, we discover heat shock protein 60 (Hsp60) as a vital target of ATO. Through biophysical and cell-based assays, we demonstrate that ATO binds to Hsp60, leading to abolishment of Hsp60 refolding capability. Significantly, the binding of ATO to Hsp60 disrupts the formation of Hsp60-p53 and Hsp60-survivin complexes, resulting in degradation of p53 and survivin. This study provides significant insights into the mechanism of action of ATO at a systemic perspective, and serves as guidance for the rational design of metal-based anticancer drugs.

BING, a novel antimicrobial peptide isolated from Japanese medaka plasma, targets bacterial envelope stress response by suppressing cpxR expression.

Antimicrobial peptides (AMPs) have emerged as a promising alternative to small molecule antibiotics. Although AMPs have previously been isolated in many organisms, efforts on the systematic identification of AMPs in fish have been lagging. Here, we collected peptides from the plasma of medaka (Oryzias latipes) fish. By using mass spectrometry, 6399 unique sequences were identified from the isolated peptides, among which 430 peptides were bioinformatically predicted to be potential AMPs. One of them, a thermostable 13-residue peptide named BING, shows a broad-spectrum toxicity against pathogenic bacteria including drug-resistant strains, at concentrations that presented relatively low toxicity to mammalian cell lines and medaka. Proteomic analysis indicated that BING treatment induced a deregulation of periplasmic peptidyl-prolyl isomerases in gram-negative bacteria. We observed that BING reduced the RNA level of cpxR, an upstream regulator of envelope stress responses. cpxR is known to play a crucial role in the development of antimicrobial resistance, including the regulation of genes involved in drug efflux. BING downregulated the expression of efflux pump components mexB, mexY and oprM in P. aeruginosa and significantly synergised the toxicity of antibiotics towards these bacteria. In addition, exposure to sublethal doses of BING delayed the development of antibiotic resistance. To our knowledge, BING is the first AMP shown to suppress cpxR expression in Gram-negative bacteria. This discovery highlights the cpxR pathway as a potential antimicrobial target.

Prognostic value of non-smoking, non-alcohol drinking status in oral cavity cancer.

OBJECTIVES: To compare the treatment response and prognosis of oral cavity cancer between non-smoking and non-alcohol-drinking (NSND) patients and smoking and alcohol-drinking (SD) patients. METHODS: A total of 313 consecutively treated patients from 2000 to 2019 were included. Demographic, clinicopathologic, treatment, and prognosis information were obtained. Relapse-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) were compared between NSND and SD groups using Kaplan-Meier plots, log-rank test, and multivariate Cox regression analysis. RESULTS: Sample prevalence of NSND patients was 54.6%. These patients were predominantly females in their eighth decade with lower prevalence of floor of the mouth cancers compared to SD patients (1.8% vs 14.8%). No difference in the RFS and DSS between both groups was found following multivariable analysis; however, NSND patients had better OS (HR (95% CI) - 0.47 (0.29-0.75); p = 0.002). Extracapsular extension was associated with significantly poorer OS, DSS, and RFS in this oral cavity cancer cohort. CONCLUSION: Treatment response and disease-specific prognosis are comparable between NSND and SD patients with oral cavity cancer. However, NSND patients have better OS. CLINICAL RELEVANCE: This study shows that oral cavity cancer in NSND is not less or more aggressive compared to SD patients. Although better survival is expected for NSND than SD patients, this is likely due to the reduced incidence of other chronic diseases in the NSND group.