Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model.

Laryngeal cancer (LC) represents a substantial world health problem, with diminished survival rates attributed to late-stage diagnoses. Correct treatment for LC is complex, particularly in the final stages. This kind of cancer is a complex malignancy inside the head and neck region of patients. Recently, researchers serving medical consultants to recognize LC efficiently develop different analysis methods and tools. However, these existing tools and techniques have various problems regarding performance constraints, like lesser accuracy in detecting LC at the early stages, additional computational complexity, and colossal time utilization in patient screening. Deep learning (DL) approaches have been established that are effective in the recognition of LC. Therefore, this study develops an efficient LC Detection using the Chaotic Metaheuristics Integration with the DL (LCD-CMDL) technique. The LCD-CMDL technique mainly focuses on detecting and classifying LC utilizing throat region images. In the LCD-CMDL technique, the contrast enhancement process uses the CLAHE approach. For feature extraction, the LCD-CMDL technique applies the Squeeze-and-Excitation ResNet (SE-ResNet) model to learn the complex and intrinsic features from the image preprocessing. Moreover, the hyperparameter tuning of the SE-ResNet approach is performed using a chaotic adaptive sparrow search algorithm (CSSA). Finally, the extreme learning machine (ELM) model was applied to detect and classify the LC. The performance evaluation of the LCD-CMDL approach occurs utilizing a benchmark throat region image database. The experimental values implied the superior performance of the LCD-CMDL approach over recent state-of-the-art approaches.

A comprehensive study on the impact of human hair fiber and millet husk ash on concrete properties: response surface modeling and optimization.

Revolutionizing construction, the concrete blend seamlessly integrates human hair (HH) fibers and millet husk ash (MHA) as a sustainable alternative. By repurposing human hair for enhanced tensile strength and utilizing millet husk ash to replace sand, these materials not only reduce waste but also create a durable, eco-friendly solution. This groundbreaking methodology not only adheres to established structural criteria but also advances the concepts of the circular economy, representing a significant advancement towards environmentally sustainable and resilient building practices. The main purpose of the research is to investigate the fresh and mechanical characteristics of concrete blended with 10-40% MHA as a sand substitute and 0.5-2% HH fibers by applying response surface methodology modeling and optimization. A comprehensive study involved preparing 225 concrete specimens using a mix ratio of 1:1.5:3 with a water-to-cement ratio of 0.52, followed by a 28 day curing period. It was found that a blend of 30% MHA and 1% HH fibers gave the best compressive and splitting tensile strengths at 28 days, which were 33.88 MPa and 3.47 MPa, respectively. Additionally, the incorporation of increased proportions of MHA and HH fibers led to reductions in both the dry density and workability of the concrete. In addition, utilizing analysis of variance (ANOVA), response prediction models were created and verified with a significance level of 95%. The models' R2 values ranged from 72 to 99%. The study validated multi-objective optimization, showing 1% HH fiber and 30% MHA in concrete enhances strength, reduces waste, and promotes environmental sustainability, making it recommended for construction.

FRailty in Australian patients admitted to Intensive care unit after eLective CANCER-related SURGery: a retrospective multicentre cohort study (FRAIL-CANCER-SURG study).

BACKGROUND: The association between frailty and short-term and long-term outcomes in patients receiving elective surgery for cancer remains unclear, particularly in those admitted to the ICU. METHODS: In this multicentre retrospective cohort study, we included adults ≥16 yr old admitted to 158 ICUs in Australia from January 1, 2018 to March 31, 2022 after elective surgery for cancer. We investigated the association between frailty and survival time up to 4 yr (primary outcome), adjusting for a prespecified set of covariates. We analysed how this association changed in specific subgroups (age categories [<65, 65-80, ≥80 yr], and those who survived hospitalisation), and over time by splitting the survival information at monthly intervals. RESULTS: We included 35,848 patients (median follow-up: 18.1 months [inter-quartile range: 8.3-31.1 months], 19,979 [56.1%] male, median age 69.0 yr [inter-quartile range: 58.8-76.0 yr]). Some 3502 (9.8%) patients were frail (defined as clinical frailty scale ≥5). Frailty was associated with lower survival (hazard ratio: 1.72, 95% confidence interval [CI]: 1.59-1.86 compared with clinical frailty scale ≤4); this was concordant across several sensitivity analyses. Frailty was most strongly associated with mortality early on in follow-up, up to 10 months (hazard ratio: 1.39, 95% CI: 1.03-1.86), but this association plateaued, and its predictive capacity subsequently diminished with time up until 4 yr (1.96, 95% CI: 0.73-5.28). Frailty was associated with similar effects when stratified based on age, and in those who survived hospitalisation. CONCLUSIONS: Frailty was associated with poorer outcomes after an ICU admission after elective surgery for cancer, particularly in the short term. However, its predictive capacity with time diminished, suggesting a potential need for longitudinal reassessment to ensure appropriate prognostication in this population.

Oncology and intensive care doctors' perception of intensive care admission of cancer patients: A cross-sectional national survey.

INTRODUCTION: Prognosis in oncology has improved with early diagnosis and novel therapies. However, critical illness continues to trigger clinical and ethical dilemmas for the treating oncology and intensive care unit (ICU) doctors. OBJECTIVES: The objective of this study was to investigate the perceptions of oncology and ICU doctors in managing critically ill cancer patients. METHODS: A cross-sectional web-based survey exploring the management of a fictitious acutely deteriorating case vignette with solid-organ malignancy. The survey weblink was distributed between May and July 2022 to all Australian oncology and ICU doctors via newsletters to the members of the Medical Oncology Group of Australia, the Australian and New Zealand Intensive Care Society, and the College of Intensive Care Medicine inviting them to participate. The weblink was active till August 2022. The six domains included patient prognostication, advanced care plan, collaborative management, legal/ethical/moral challenges, ICU referral, and protocol-based ICU admission. The outcomes were reported as the level of agreement between oncology and ICU doctors for each domain/question. RESULTS: 184 responses (64 oncology and 120 ICU doctors) were analysed. Most respondents were specialists (78.1% [n = 50] oncology, 78.3% [n = 94] ICU doctors). Oncology doctors more commonly reported managing cancer patients with poor prognosis than ICU doctors (p < 0.001). Oncology doctors less commonly referred such patients for ICU admission (29.7% [n = 19] vs. 80.8% [n = 97], p < 0.001; odds ratio [OR] = 0.07; 95% confidence interval [CI]: 0.03-0.16) and infrequently encountered patients with prior goals of care (GOC) in medical emergency team escalations (40.6% [n = 26] vs. 86.7% [n = 104]; p < 0.001; OR = 0.06; 95% CI: 0.02-0.15; p < 0.001). Oncology doctors were less likely to discuss GOC during medical emergency team calls or within 24 h of ICU admission. More oncology doctors than ICU doctors thought that training rotation in the corresponding speciality group was beneficial (56.3% [n = 36] vs. 31.7% [n = 38]; p = 0.012; OR = 2.07; 95% CI: 1.02-4.23; p = 0.045). CONCLUSION: Oncology doctors were less likely to encounter acute patient deterioration or establish timely GOC for such patients. Oncology doctors believed that an ICU rotation during their training may have helped manage challenging situations.

Immune checkpoint inhibitor therapy for advanced cutaneous squamous cell carcinoma in Australia: a retrospective real world cohort study.

OBJECTIVES: To review the outcomes of immune checkpoint inhibitor (ICI) treatment of advanced cutaneous squamous cell carcinoma (CSCC) outside clinical trials. STUDY DESIGN: Retrospective observational study; review of patient records in fifteen Australian institutions. SETTING, PARTICIPANTS: All Australian adults with locally advanced or metastatic CSCC not amenable to curative surgery or radiotherapy treated with ICIs, 5 May 2017 - 23 May 2022, through a cemiplimab compassionate access scheme (Therapeutic Goods Administration Special Access Scheme) or who personally covered the cost of pembrolizumab prior to the start of the access scheme. MAIN OUTCOME MEASURES: Best overall response rate (ORR) according to standardised assessment criteria using the hierarchy: Response Evaluation Criteria in Solid Tumors (RECIST 1.1), the modified World Health Organization clinical response criteria, and the Positron Emission Tomography Response Criteria (PERCIST 1.0); overall and progression-free survival. RESULTS: A total of 286 people with advanced CSCC received ICI therapy during May 2017 - May 2022 (cemiplimab, 270; pembrolizumab, 16). Their median age was 75.2 years (range, 39.3-97.5 years) and 232 were men (81%); median follow-up time was 12.2 months (interquartile range, 5.5-20.5 months). Eighty-eight people (31%) were immunocompromised, 27 had autoimmune disease, and 59 of 277 (21%) had ECOG performance scores of 2 or 3. The ORR was 60% (166 of 278 evaluable patients): complete responses were recorded for 74 (27%) and partial responses for 92 patients (33%). Twelve-month overall survival was 78% (95% confidence interval [CI], 72-83%); progression-free survival was 65% (95% CI, 58-70%). Poorer ECOG performance status was associated with poorer overall survival (per unit: adjusted hazard ratio [aHR], 3.0; 95% CI, 2.0-4.3) and progression-free survival (aHR, 2.4; 95% CI, 1.8-3.3), as was being immunocompromised (overall: aHR, 1.8; 95% CI, 1.1-3.0; progression-free: aHR, 1.8; 95% CI, 1.2-2.7). Fifty-five people (19%) reported immune-related adverse events of grade 2 or higher; there were no treatment-related deaths. CONCLUSION: In our retrospective study, the effectiveness and toxicity of ICI therapy were similar to those determined in clinical trials. Our findings suggest that ICIs could be effective and well tolerated by people with advanced CSCC who are ineligible for clinical trials.

Atherosclerotic Cardiovascular Events in Cancer Patients Treated With Immune Checkpoint Inhibitors: A Retrospective Cohort Study.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are effective therapies for numerous cancers, but have been associated with atherosclerotic cardiovascular disease (ASCVD). This study aimed to identify predictors for ASCVD events among cancer patients treated with ICIs and the cardiovascular risk factor (CVRF) control of those who developed ASCVD. METHOD: A single-centre retrospective study of 366 cancer patients who received ICIs from 2018 to 2020 was performed. Demographic, baseline CVRF, cancer history, and ICI regimen data were obtained from medical records. The primary end point of ASCVD events was defined as myocardial infarction, coronary revascularisation, ischaemic stroke, or acute limb ischaemia. Cox proportional multivariable modelling and competing risks analysis were performed to assess ASCVD predictors. Descriptive analysis was performed to describe CVRF management among those who developed ASCVD events. RESULTS: Over a median follow-up of 3.4 years (2.8-4.3), 26 patients (7.1%) experienced 27 ASCVD events (seven myocardial infarction, one coronary revascularisation, 13 ischaemic stroke, and six acute limb ischaemia events). There were 226 (61.8%) cancer-related deaths and no cardiac deaths. History of ASCVD before ICI initiation was independently associated with ASCVD events on traditional Cox modelling (hazard ratio [HR] 4.00; 95% confidence interval [CI] 1.79-8.91; p<0.01) and competing risks analysis (HR 4.23; 95% CI 1.87-9.60; p<0.01). A total of 17 patients developed ASCVD events after ICI cessation (median 1.4 years). Among those with ASCVD events, 12 had prior ASCVD, 16 had hypertension, nine had hypercholesterolaemia, and four had diabetes, and nine were actively smoking. Variable prescription of cardiovascular preventative therapies was noted. CONCLUSIONS: History of ASCVD was associated with subsequent ASCVD events among patients treated with ICIs, which could occur even after active treatment was stopped. Identification and aggressive management of modifiable CVRFs should be considered throughout cancer survivorship in patients who received ICI treatment.

Frailty and long-term survival among patients in Australian intensive care units with metastatic cancer (FRAIL-CANCER study): a retrospective registry-based cohort study.

BACKGROUND: Recent advances in cancer therapeutics have improved outcomes, resulting in increasing candidacy of patients with metastatic cancer being admitted to intensive care units (ICUs). A large proportion of patients also have frailty, predisposing them to poor outcomes, yet the literature reporting on this is scarce. We aimed to assess the impact of frailty on survival in patients with metastatic cancer admitted to the ICU. METHODS: In this retrospective registry-based cohort study, we used data from the Australia and New Zealand Intensive Care Society Adult Patient (age ≥16 years) database to identify patients with advanced (solid and haematological cancer) and a documented Clinical Frailty scale (CFS) admitted to 166 Australian ICUs. Patients without metastatic cancer were excluded. We analysed the effect of frailty (CFS 5-8) on long-term survival, and how this effect changed in specific subgroups (cancer subtypes, age [<65 years or ≥65 years], and those who survived hospitalisation). Because estimates tend to cluster within centres and vary between them, we used Cox proportional hazards regression models with robust sandwich variance estimators to assess the effect of frailty on survival time up to 4 years after ICU admission between groups. FINDINGS: Between Jan 1, 2018, and March 31, 2022, 30 026 patients were eligible, and after exclusions 21 174 patients were included in the analysis; of these, 6806 (32·1%) had frailty, and 11 662 (55·1%) were male, 9489 (44·8%) were female, and 23 (0·1%) were intersex or self-reported indeterminate sex. The overall survival was lower for patients with frailty at 4 years compared with patients without frailty (29·5% vs 10·9%; p<0·0001). Frailty was associated with shorter 4-year survival times (adjusted hazard ratio 1·52 [95% CI 1·43-1·60]), and this effect was seen across all cancer subtypes. Frailty was associated with shorter survival times in patients younger than 65 years (1·66 [1·51-1·83]) and aged 65 years or older (1·40 [1·38-1·56]), but its effects were larger in patients younger than 65 years (pinteraction<0·0001). Frailty was also associated with shorter survival times in patients who survived hospitalisation (1·49 [1·40-1·59]). INTERPRETATION: In patients with metastatic cancer admitted to the ICU, frailty was associated with poorer long-term survival. Patients with frailty might benefit from a goal-concordant time-limited trial in the ICU and will need suitable post-intensive care supportive management. FUNDING: None.

Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification.

Lung cancer is the main cause of cancer deaths all over the world. An important reason for these deaths was late analysis and worse prediction. With the accelerated improvement of deep learning (DL) approaches, DL can be effectively and widely executed for several real-world applications in healthcare systems, like medical image interpretation and disease analysis. Medical imaging devices can be vital in primary-stage lung tumor analysis and the observation of lung tumors from the treatment. Many medical imaging modalities like computed tomography (CT), chest X-ray (CXR), molecular imaging, magnetic resonance imaging (MRI), and positron emission tomography (PET) systems are widely analyzed for lung cancer detection. This article presents a new dung beetle optimization modified deep feature fusion model for lung cancer detection and classification (DBOMDFF-LCC) technique. The presented DBOMDFF-LCC technique mainly depends upon the feature fusion and hyperparameter tuning process. To accomplish this, the DBOMDFF-LCC technique uses a feature fusion process comprising three DL models, namely residual network (ResNet), densely connected network (DenseNet), and Inception-ResNet-v2. Furthermore, the DBO approach was employed for the optimum hyperparameter selection of three DL approaches. For lung cancer detection purposes, the DBOMDFF-LCC system utilizes a long short-term memory (LSTM) approach. The simulation result analysis of the DBOMDFF-LCC technique of the medical dataset is investigated using different evaluation metrics. The extensive comparative results highlighted the betterment of the DBOMDFF-LCC technique of lung cancer classification.

Real-World Analysis of Clinical Characteristics and Survival Outcomes in Patients With Extensive-Stage SCLC Treated With First-Line Chemoimmunotherapy.

Introduction: There are no clinically validated prognostic biomarkers in the management of extensive-stage SCLC (ES-SCLC). We explored the association between clinical characteristics and survival outcomes in patients with ES-SCLC treated with chemoimmunotherapy. Methods: In this retrospective cohort study, patients with ES-SCLC treated with first-line platinum-etoposide chemotherapy and atezolizumab were identified from medical records. Pretreatment clinical characteristics, biochemical parameters, and tumor and treatment characteristics were collected. Univariate and multivariate Cox regression were used to evaluate treatment effect on progression-free survival (PFS) and overall survival (OS). Results: We evaluated 75 patients in total. The median PFS and OS were 6.1 months and 9.2 months, respectively. Statistically significant associations were found with lower lactate dehydrogenase and improved OS (hazard ratio [HR] = 1.0, 95% confidence interval [CI]: 1.0-1.01, p = 0.006), whereas higher age (HR = 0.94, 95% CI: 0.90-0.98, p = 0.006) and lower neutrophil-to-lymphocyte ratio (HR = 1.08, 95% CI: 1.02-1.14, p = 0.005) were associated with improved PFS. The number of chemotherapy cycles received were associated with both an improved PFS (HR = 0.57, 95% CI: 0.37-0.89, p = 0.011) and OS (HR = 0.5, 95% CI: 0.30-0.84, p = 0.008). Conclusions: This study highlights the important effect of chemotherapy on survival. Furthermore, the association between lactate dehydrogenase and neutrophil-to-lymphocyte ratio on survival further suggests that baseline tumor burden and optimizing sarcopenia are important factors for clinicians to consider as we seek to develop personalized treatment for this disease.

Circulating Tumour DNA (ctDNA) as a Predictor of Clinical Outcome in Non-Small Cell Lung Cancer Undergoing Targeted Therapies: A Systematic Review and Meta-Analysis.

BACKGROUND: Liquid biopsy (LB) analysis using (ctDNA)/cell-free DNA (cfDNA) is an emerging alternative to tissue profiling in (NSCLC). LB is used to guide treatment decisions, detect resistance mechanisms, and predicts responses, and, therefore, outcomes. This systematic review and meta-analysis evaluated the impact of LB quantification on clinical outcomes in molecularly altered advanced NSCLC undergoing targeted therapies. METHODS: We searched Embase, MEDLINE, PubMed, and Cochrane Database, between 1 January 2020 and 31 August 2022. The primary outcome was progression-free survival (PFS). Secondary outcomes included overall survival (OS), objective response rate (ORR), sensitivity, and specificity. Age stratification was performed based on the mean age of the individual study population. The quality of studies was assessed using the Newcastle-Ottawa Scale (NOS). RESULTS: A total of 27 studies (3419 patients) were included in the analysis. Association of baseline ctDNA with PFS was reported in 11 studies (1359 patients), while that of dynamic changes with PFS was reported in 16 studies (1659 patients). Baseline ctDNA-negative patients had a trend towards improved PFS (pooled hazard ratio [pHR] = 1.35; 95%CI: 0.83-1.87; p < 0.001; I2 = 96%) than ctDNA-positive patients. Early reduction/clearance of ctDNA levels after treatment was related to improved PFS (pHR = 2.71; 95%CI: 1.85-3.65; I2 = 89.4%) compared to those with no reduction/persistence in ctDNA levels. The sensitivity analysis based on study quality (NOS) demonstrated improved PFS only for good [pHR = 1.95; 95%CI: 1.52-2.38] and fair [pHR = 1.99; 95%CI: 1.09-2.89] quality studies, but not for poor quality studies. There was, however, a high level of heterogeneity (I2 = 89.4%) along with significant publication bias in our analysis. CONCLUSIONS: This large systematic review, despite heterogeneity, found that baseline negative ctDNA levels and early reduction in ctDNA following treatment could be strong prognostic markers for PFS and OS in patients undergoing targeted therapies for advanced NSCLC. Future randomised clinical trials should incorporate serial ctDNA monitoring to further establish the clinical utility in advanced NSCLC management.

MYC drives platinum resistant SCLC that is overcome by the dual PI3K-HDAC inhibitor fimepinostat.

BACKGROUND: Small cell lung cancer (SCLC) is an aggressive neuroendocrine cancer with an appalling overall survival of less than 5% (Zimmerman et al. J Thor Oncol 14:768-83, 2019). Patients typically respond to front line platinum-based doublet chemotherapy, but almost universally relapse with drug resistant disease. Elevated MYC expression is common in SCLC and has been associated with platinum resistance. This study evaluates the capacity of MYC to drive platinum resistance and through screening identifies a drug capable of reducing MYC expression and overcoming resistance. METHODS: Elevated MYC expression following the acquisition of platinum resistance in vitro and in vivo was assessed. Moreover, the capacity of enforced MYC expression to drive platinum resistance was defined in SCLC cell lines and in a genetically engineered mouse model that expresses MYC specifically in lung tumors. High throughput drug screening was used to identify drugs able to kill MYC-expressing, platinum resistant cell lines. The capacity of this drug to treat SCLC was defined in vivo in both transplant models using cell lines and patient derived xenografts and in combination with platinum and etoposide chemotherapy in an autochthonous mouse model of platinum resistant SCLC. RESULTS: MYC expression is elevated following the acquisition of platinum resistance and constitutively high MYC expression drives platinum resistance in vitro and in vivo. We show that fimepinostat decreases MYC expression and that it is an effective single agent treatment for SCLC in vitro and in vivo. Indeed, fimepinostat is as effective as platinum-etoposide treatment in vivo. Importantly, when combined with platinum and etoposide, fimepinostat achieves a significant increase in survival. CONCLUSIONS: MYC is a potent driver of platinum resistance in SCLC that is effectively treated with fimepinostat.

Leveraging Marine Predators Algorithm with Deep Learning for Lung and Colon Cancer Diagnosis.

Cancer is a deadly disease caused by various biochemical abnormalities and genetic diseases. Colon and lung cancer have developed as two major causes of disability and death in human beings. The histopathological detection of these malignancies is a vital element in determining the optimal solution. Timely and initial diagnosis of the sickness on either front diminishes the possibility of death. Deep learning (DL) and machine learning (ML) methods are used to hasten such cancer recognition, allowing the research community to examine more patients in a much shorter period and at a less cost. This study introduces a marine predator's algorithm with deep learning as a lung and colon cancer classification (MPADL-LC3) technique. The presented MPADL-LC3 technique aims to properly discriminate different types of lung and colon cancer on histopathological images. To accomplish this, the MPADL-LC3 technique employs CLAHE-based contrast enhancement as a pre-processing step. In addition, the MPADL-LC3 technique applies MobileNet to derive feature vector generation. Meanwhile, the MPADL-LC3 technique employs MPA as a hyperparameter optimizer. Furthermore, deep belief networks (DBN) can be applied for lung and color classification. The simulation values of the MPADL-LC3 technique were examined on benchmark datasets. The comparison study highlighted the enhanced outcomes of the MPADL-LC3 system in terms of different measures.

Treatment outcomes of standard (high dose) cisplatin and non-standard chemotherapy for locally advanced head and neck cancer.

INTRODUCTION: Concurrent chemoradiotherapy with high-dose (HD) cisplatin is the standard treatment for locally advanced head and neck squamous cell carcinoma (LA-HNSCC). Due to the higher treatment-related adverse effects with standard therapy, alternative regimens (non-standard therapy), namely, lower dose weekly cisplatin, carboplatin/paclitaxel, or cetuximab are considered. There is, however, no consensus on non-standard regimens. We aimed to investigate the efficacy and safety profile of these regimens. METHODS: This single centre retrospective cohort study included all consecutive adult patients with newly diagnosed LA-HNSCC treated with either standard or non-standard regimens between January 2016 and April 2021. The primary outcome was 2-year failure-free survival (FFS). The secondary outcomes included acute toxicities, hospitalisation rates, dose modifications, treatment failure rates (TFR), and overall survival. RESULTS: About 235 patients were included in the final analysis; median age was 61 years (IQR 55-67), and 87% were male. Most had oropharyngeal tumours (85.5%) and p16-positivity was frequent (80%). About 56% received non-standard regimens: weekly cisplatin = 79 and non-cisplatin = 48. These patients had higher Charlson Comorbidity Index (CCI; p < .001) and lower European Cooperative Oncology Group (ECOG)-0 (p = .003). There was no difference in 2-year FFS (hazard ratio [HR] = 1.16; 95% confidence interval - [CI] 0.65-2.05), hospitalisation and grade-3 toxicity rates between the two regimens. Nausea and vomiting were lower in the non-standard regimen (3.0% vs. 16%, p < .001). Dose reductions, adjusted for age, sex, and CCI, were less likely in the non-standard regimen (OR = 2.36; 95%-CI: 1.01-5.49, p = .007). CONCLUSIONS: We demonstrated similar efficacy of lower dose weekly cisplatin and carboplatin/paclitaxel regimens and better safety profile of weekly cisplatin compared to standard HD cisplatin regimens for LA-HNSCC. Multicenter randomised control trials are required in HD cisplatin-ineligible patients.

Gastroprotective potential and mechanisms of action of Hedera nepalensis

Abstract Hedera nepalensis (H. nepalensis) , belonging to the family Araliaceae, is a medicinal plant traditionally used to treat stomach problems. The current study investigated the gastroprotective potential and the mechanism of action of H. nepalensis in diclofenac-and ethanol-induced ulcer models. Anti-oxidant and lipid peroxidation inhibitory prospects of H. nepalensis were checked out by free radical scavenging assay and UV spectrophotometer respectively. Effect of H. nepalensis on the pH, gastric total acidity of gastric juice and protective effects of H. nepalensis against ulcer models have been examined. Histopathological studies have been carried out. The aqueous methanol extract of H. nepalensis (100 µg/mL) showed anti-oxidant (83.55%) and lipid peroxidation inhibitory (70.88%) potential at 1000 µg/mL; the extract had no buffer potential. The extract (400 mg/kg) significantly (81.12% and 63.46%) showed gastroprotective effect in diclofenac and ethanol-induced rat ulcer models respectively. Histopathological studies confirmed the biochemical findings. FTIR analysis showed the presence of carboxylic acid, alkanes, conjugated alkanes, aldehydes and alkyl-aryl ethers. Gallic acid, M-coumaric acid and quercetin were found by HPLC analysis. H. nepalensis exhibited significant protection against diclofenac and ethanol induced gastric damage by anti-oxidant and lipid peroxidation suppression effects suggesting potential broad utility in treatment of diseases characterized with gastric damage.

Comparison of PD-L1 Expression in Oral Squamous Cell Carcinoma and Premalignant Lesions of Oral Cavity.

OBJECTIVE: Objectives of this study were to compare  expression of Programmed Death-Ligand 1(PD-L1) protein in oral squamous cell carcinoma (OSCC) and oral potentially malignant disorder (OPMD) cases; and to compare the PD-L1 protein expression in histological grades of OSCC and also in OPMD's with Dysplasia and without Dysplasia. MATERIALS & METHODS: In this study,  25 cases of Oral squamous cell carcinoms, 25 cases of Oral Potentially Malignant Disorders and 10 cases of non-neoplastic oral mucosa (control) cases were included. FFPE blocks of OSCC and OPMD cases were contributed by Department of Pathology, Histopathology Division,Pakistan Institute of Medical Sciences, Islamabad. Immunohistochemical staining of cases with PD-L1 monoclonal antibody (1:100; Dako) was carried out at Histopathology division , PMC Labs,  Peshawar Medical College,Peshawar, Riphah International University, Islamabad . Epithelial cells (membranous and cytoplasmic) positivity was observed for PD-L1 Antibody. Data was analyzed in SPSS version20. For qualitative variables frequencies and percentages were calculated whereas for quantitative variables means and standard deviations were recorded. The Chi-square test was applied to evaluate the significant difference in categorical variables . p-value of ≤0.05 was taken as significant. RESULTS: PD-L1 expression in OSCC cases turned out to be 48% (n=12/25) as compared to 8% of OPMD cases (n=2/25) with a significant p value of 0.002 and all non-neoplastic oral mucosa cases were negative. PD-L1 expression in high grade OSCC cases was quite high (61% n=11/18) as compared to low grade OSCC (14% n=1/7) cases with a significant p value of 0.035. CONCLUSION: A statistically significant increased PD-L1 expression was noted in  OSCC as compared to OPMD. Expression of PD-L1 was more intense in high grade OSCC cases. The relation of PD-L1 expression to age ,gender or location of OSCC and OPMD cases , and presence of dysplasia in OPMD cases was statistically not significant.

Artificial Intelligence Driven Biomedical Image Classification for Robust Rheumatoid Arthritis Classification.

Recently, artificial intelligence (AI) including machine learning (ML) and deep learning (DL) models has been commonly employed for the automated disease diagnosis process. AI in biological and biomedical imaging is an emerging area and will be a future trend in the field. At the same time, biomedical images can be used for the classification of Rheumatoid arthritis (RA) diseases. RA is an autoimmune illness that affects the musculoskeletal system causing systemic, inflammatory and chronic effects. The disease frequently becomes progressive and decreases physical function, causing articular damage, suffering, and fatigue. After a time, RA causes harm to the cartilage of the joints and bones, weakens the tendons and joints, and finally causes joint destruction. Sensors (thermal infrared camera sensor, accelerometers and wearable sensors) are more commonly employed to collect data for RA. This study develops an Automated Rheumatoid Arthritis Classification using an Arithmetic Optimization Algorithm with Deep Learning (ARAC-AOADL) model. The goal of the presented ARAC-AOADL technique lies in the classification of health disorders depending upon RA and orthopaedics. Primarily, the presented ARAC-AOADL technique pre-processes the input images by median filtering (MF) technique. Then, the ARAC-AOADL technique uses AOA with an enhanced capsule network (ECN) model to produce feature vectors. For RA classification, the ARAC-AOADL technique uses a multi-kernel extreme learning machine (MKELM) model. The experimental result analysis of the ARAC-AOADL technique on a benchmark dataset reported a maximum accuracy of 98.57%. Therefore, the ARAC-AOADL technique can be employed for accurate and timely RA classification.

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI.

Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.

Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification.

Recently, bioinformatics and computational biology-enabled applications such as gene expression analysis, cellular restoration, medical image processing, protein structure examination, and medical data classification utilize fuzzy systems in offering effective solutions and decisions. The latest developments of fuzzy systems with artificial intelligence techniques enable to design the effective microarray gene expression classification models. In this aspect, this study introduces a novel feature subset selection with optimal adaptive neuro-fuzzy inference system (FSS-OANFIS) for gene expression classification. The major aim of the FSS-OANFIS model is to detect and classify the gene expression data. To accomplish this, the FSS-OANFIS model designs an improved grey wolf optimizer-based feature selection (IGWO-FS) model to derive an optimal subset of features. Besides, the OANFIS model is employed for gene classification and the parameter tuning of the ANFIS model is adjusted by the use of coyote optimization algorithm (COA). The application of IGWO-FS and COA techniques helps in accomplishing enhanced microarray gene expression classification outcomes. The experimental validation of the FSS-OANFIS model has been performed using Leukemia, Prostate, DLBCL Stanford, and Colon Cancer datasets. The proposed FSS-OANFIS model has resulted in a maximum classification accuracy of 89.47%.

Novel acetamide derivatives of 2-aminobenzimidazole prevent inflammatory arthritis in rats via suppression of pro-inflammatory mediators.

Benzimidazole ring system is an important pharmacophore with diverse pharmacological activities. In this study, we explored the anti-arthritic effects of newly synthesized acetamide derivatives of 2-aminobenzimidazole (N1 and N2) in rats. FTIR and NMR spectroscopies were used to characterize these compounds. Carrageenan (CRG) induced paw edema model was used to test the acute anti-inflammatory activity of various doses (10, 20 and 30 mg/kg) of N1 and N2 compounds. Based on acute anti-inflammatory effects, the most potent dose of each compound was selected and investigated in complete freund's adjuvant (CFA) induced inflammatory arthritis (RA) model (n = 4 in each group). Histopathological, hematological, radiographic, and RT-qPCR analyses were performed to assess the progression or resolution of inflammatory arthritis. The tested compounds produced a dose-dependent anti-inflammatory activity against CRG induced paw inflammation and similarly reduced edema in CFA induced inflammatory arthritis model. Histopathological and X-ray analyses of ankle joints revealed minimal inflammation and normal joint structures in N1 and N2 treated groups. The tested compounds also reduced the levels of autoantibodies and restored hematological parameters. Interestingly, the tested compounds did not elevate aspartate aminotransferase and alanine transaminase levels and displayed a better safety profile than methotrexate. N1 and N2 compounds also attenuated the transcript levels of IRAK1, NF-kB1, TNF-α, IL-1ß, IL17 and MMP1. In addition, N1 displayed a greater inhibition of mRNA levels of COX1, COX2, mPGES1 and PTGDS as compared to N2. Our findings demonstrate that N1 and N2 compounds possess strong anti-arthritic activity which can be attributed to the suppression of pro-inflammatory mediators.

Pharmacological evaluation of cardioprotective potential of Berberis orthobotrys Bien.ex Aitch.

The resurgence of scrutiny in plant-based medicine is mainly due to the current widespread belief that "green medicine" is safe and more dependable than the expensive synthetic drugs. The current study was focused to evaluate the anti-myocardial ischemic potential of Berberis orthobotrys Bien ex Aitch against chemically induced myocardial ischemia in animal models. Myocardial ischemia was instigated in Sprague Dawley rats of either sex (250-450g) by administration of Isoproterenol (ISO) and doxorubicin (DOX) at doses of 25mg/kg b.w and 15mg/kg b.w. respectively. The protective effect of the plant extract was explored by pretreating a group of animals with aqueous methanolic extract of Berberis orthobotrys roots at a dose of 50mg/kg b.w. (orally) for 10 days in ISO-ischemic model while for doxorubicin ischemic model; the study was conducted for 14 days. The findings of the study revealed that serum levels of cardiac marker enzymes were significantly increased (p<0.0001) followed by the administration of Isoproterenol and doxorubicin whereas the pretreatment with aqueous methanolic plant extract had significantly (p<0.0001) prevented the rise in the same, as compared to both intoxicated groups. The statistical analysis of the study led to the conclusion that Berberis orthobotrys possesses cardio protective potential against chemically induced myocardial ischemia.