Correlation between allergic rhinitis or hay fever and lung cancer: A systematic review and meta-analysis.

BACKGROUND: The association between allergies and cancer is contradictory, whereas some forms of cancer have inverse associations with allergies. Allergic rhinitis (AR) is the most prevalent form of allergy, and lung cancer is one of the most prevalent forms of cancer with the highest mortality rate. Recent studies have reported a positive association between asthma and lung cancer; however, this association is inconclusive. Furthermore, AR is positively associated with asthma; therefore, our research question was to explore whether there is any correlation between AR and lung cancer epidemiologically. METHODS: After a rigorous search of PubMed, Google Scholar, and ScienceDirect, 7 eligible articles were included in this systematic review and meta-analysis, including 4724 cases and 9059 controls, 5 from the USA, and one each from Canada and Germany. RESULTS: Pooled analysis (OR, 0.55; 95% CI: 0.45-0.68; P value < .00001) showed a strong inverse relationship between AR and lung cancer. CONCLUSION: The current meta-analysis suggests an inverse relationship between AR and lung cancer; however, new epidemiological studies are required to observe the current scenario more comprehensively.

Exploring the Relationship of Comorbidities, Smoking Status, HRCT Findings With COVID-19 Disease Severity and Outcomes.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) is a serious illness that can affect multiple organs including the lungs. The COVID-mortality risk is attributed to the quick transmission of the virus, the severity of disease, and preclinical risk factors, such as the presence of comorbidities. High-resolution computed tomography (HRCT) can predict disease severity in COVID-19 patients. METHODOLOGY: This was a retrospective cohort study in which data were obtained from COVID centers at tertiary care hospitals in Azad Jammu and Kashmir. Details of clinical characteristics and HRCT findings along with details of smoking and comorbid history were obtained. RESULTS: Fever at hospital admission, HRCT findings, and having a partner predicted disease severity showed a significant p-value of <0.05. Old age and living in a combined household were associated with severe outcomes (p<0.05). Symptoms of shortness of breath (SOB) on hospital admission could predict the need for ICU admission in COVID-19 patients. CONCLUSION: HRCT has a good predictive value for disease severity in patients with COVID-19, and old age is a risk factor. Although, limited associations were established in the analysis, in this study hyperlipidemia and hypertension significantly affected the course of disease. Further studies should be done to explore the relationship.

Effect of rosemary essential oil as nitrite substitute on quality of sausage produced using chicken fed by thymus essential oil and rapeseed oil.

The objective was to determine the effect of rosemary essential oil (REO) as nitrite substitute on the physicochemical, microbiological and sensory quality of sausage from broilers fed different levels of thymus essential oil (TEO) and rapeseed oil (RO). Broiler chicks fed TEO (0, 300 and 500 ppm) and RO (1, 3 and 5%) in a 3 × 3 factorial design. Sausages were made from meat of chicken fed TEO (0 and 500 ppm) treated with REO (120 ppm), REO:nitrite (60:60 ppm), nitrite (120 ppm) or without REO and nitrite in a 2 × 4 factorial design. Results showed that TEO increased water holding capacity and pH value and decreased malondialdehyde concentration. RO (1 and 3%) × TEO (500 ppm) group had higher unsaturated to saturated fatty acids ratio in the meat. Results showed that treatments of REO (120 ppm) decreased lipid oxidation and increased pH value of meat. Sausage produced by nitrite (120 ppm) or REO:nitrite (60:60 ppm) had lower total microbial count, lightness but higher redness and yellowness. Based on the results, TEO in chicken feed decreased lipid oxidation of sausages. Furthermore, substitution at least 60 ppm nitrite with REO had more preserving effect on quality of produced sausages.

Influence of dietary plant fats and antioxidant supplementations on performance, apparent metabolizable energy and protein digestibility, lipid oxidation and fatty acid composition of meat in broiler chicken.

This study aimed to evaluate the effects of dietary different antioxidants and plant oils on performance, apparent metabolizable energy and protein digestibility, meat quality and meat fatty acid composition of broiler chickens. In all, 480 male broiler chicks of 1-day old were assigned in a completely randomized design with factorial arrangement 2 × 5 (plant oil sources [soybean and rapeseed oils] and antioxidant sources [vitamin E, Thyme, Rosemary and Satureja essential oils] furthermore control treatment without antioxidant). The results indicated that at 1-42 d of age, growth performance and carcass yield of birds were not influenced by dietary plant oils and antioxidant supplementations. Dietary Thyme essential oil (300 mg/kg) resulted in an increase in crude protein digestibility and birds fed on diets without antioxidant showed increase in the apparent metabolizable energy (p < .01). Birds receiving the combination of soybean oil with Rosemary essential oil had lowest malondialdehyde concentration in comparison to birds receiving other treatments (p < .05) in the drumstick meat. Also, birds receiving the combination of soybean oil with vitamin E had lowest malondialdehyde concentration in comparison to birds receiving other treatments (p < .05) in the breast meat. The results indicated that treatments did not influence water holding capacity of meat. Also, dietary rapeseed oil and Thyme essential oil supplementations, separately, decreased saturated fatty acid (p < .01) and increased unsaturated fatty acid and unsaturated to saturated fatty acids ratio (p < .01) of drumstick meat tissue in broiler chicken (p < .01). In conclusion, dietary rapeseed oil and Thyme essential oil increased in n-3 polyunsaturated fatty acids in the drumstick meat (p < .01) and a combination of dietary soybean oil, Rosemary essential oil and vitamin E decreased the lipid oxidation in the meat of broiler chickens (p < .05).

Machine learning to predict rapid progression of carotid atherosclerosis in patients with impaired glucose tolerance.

OBJECTIVES: Prediabetes is a major epidemic and is associated with adverse cardio-cerebrovascular outcomes. Early identification of patients who will develop rapid progression of atherosclerosis could be beneficial for improved risk stratification. In this paper, we investigate important factors impacting the prediction, using several machine learning methods, of rapid progression of carotid intima-media thickness in impaired glucose tolerance (IGT) participants. METHODS: In the Actos Now for Prevention of Diabetes (ACT NOW) study, 382 participants with IGT underwent carotid intima-media thickness (CIMT) ultrasound evaluation at baseline and at 15-18 months, and were divided into rapid progressors (RP, n = 39, 58 ± 17.5 µM change) and non-rapid progressors (NRP, n = 343, 5.8 ± 20 µM change, p < 0.001 versus RP). To deal with complex multi-modal data consisting of demographic, clinical, and laboratory variables, we propose a general data-driven framework to investigate the ACT NOW dataset. In particular, we first employed a Fisher Score-based feature selection method to identify the most effective variables and then proposed a probabilistic Bayes-based learning method for the prediction. Comparison of the methods and factors was conducted using area under the receiver operating characteristic curve (AUC) analyses and Brier score. RESULTS: The experimental results show that the proposed learning methods performed well in identifying or predicting RP. Among the methods, the performance of Naïve Bayes was the best (AUC 0.797, Brier score 0.085) compared to multilayer perceptron (0.729, 0.086) and random forest (0.642, 0.10). The results also show that feature selection has a significant positive impact on the data prediction performance. CONCLUSIONS: By dealing with multi-modal data, the proposed learning methods show effectiveness in predicting prediabetics at risk for rapid atherosclerosis progression. The proposed framework demonstrated utility in outcome prediction in a typical multidimensional clinical dataset with a relatively small number of subjects, extending the potential utility of machine learning approaches beyond extremely large-scale datasets.

Active surveillance of leprosy contacts in country with low prevalence rate.

For advanced control of leprosy in Pakistan where the World Health Organization leprosy elimination goal was achieved in 1996, we conducted surveillance of Mycobacterium leprae-seropositive patients and their contacts and drug resistant strains of M. leprae. We measured anti-PGL-I antibody level in sera from leprosy patients and their contacts for early detection of M. leprae infection. Out of 34 leprosy patients undergoing treatment, 4 lepromatous leprosy patients were antibody positive, and 6.8 to 23.7 percent of occupational or household contacts were seropositive. Furthermore, three cases (1.2%) had a high antibody titer. For surveillance of drug resistant strains of M. leprae, dapsone and rifampin were targeted. Four out of 18 polymerase chain reaction (PCR) positive samples had mutation in folP gene, and among 10 PCR positive samples, one had a mutation in the rpoB gene. These results indicate that serological analysis of patient contacts might be useful to find out high risk individuals, and there are M. leprae strains resistant to chemotherapeutic agents in Pakistan.