Prevalence of co-existent COVID-19-associated pulmonary aspergillosis (CAPA) and its impact on early mortality in patients with COVID-19-associated pulmonary mucormycosis (CAPM).

BACKGROUND: Data on mixed mould infection with COVID-19-associated pulmonary aspergillosis (CAPA) and COVID-19-associated pulmonary mucormycosis (CAPM) are sparse. OBJECTIVES: To ascertain the prevalence of co-existent CAPA in CAPM (mixed mould infection) and whether mixed mould infection is associated with early mortality (≤7 days of diagnosis). METHODS: We retrospectively analysed the data collected from 25 centres across India on COVID-19-associated mucormycosis. We included only CAPM and excluded subjects with disseminated or rhino-orbital mucormycosis. We defined co-existent CAPA if a respiratory specimen showed septate hyphae on smear, histopathology or culture grew Aspergillus spp. We also compare the demography, predisposing factors, severity of COVID-19, and management of CAPM patients with and without CAPA. Using a case-control design, we assess whether mixed mould infection (primary exposure) were associated with early mortality in CAPM. RESULTS: We included 105 patients with CAPM. The prevalence of mixed mould infection was 20% (21/105). Patients with mixed mould infection experienced early mortality (9/21 [42.9%] vs. 15/84 [17.9%]; p = 0.02) and poorer survival at 6 weeks (7/21 [33.3] vs. 46/77 [59.7%]; p = 0.03) than CAPM alone. On imaging, consolidation was more commonly encountered with mixed mould infections than CAPM. Co-existent CAPA (odds ratio [95% confidence interval], 19.1 [2.62-139.1]) was independently associated with early mortality in CAPM after adjusting for hypoxemia during COVID-19 and other factors. CONCLUSION: Coinfection of CAPA and CAPM was not uncommon in our CAPM patients and portends a worse prognosis. Prospective studies from different countries are required to know the impact of mixed mould infection.

Risk factors, mortality, and predictors of survival in COVID-19-associated pulmonary mucormycosis: a multicentre retrospective study from India.

OBJECTIVES: To compare COVID-19-associated pulmonary mucormycosis (CAPM) with COVID-19-associated rhino-orbital mucormycosis (CAROM), ascertain factors associated with CAPM among patients with COVID-19, and identify factors associated with 12-week mortality in CAPM. METHODS: We performed a retrospective multicentre cohort study. All study participants had COVID-19. We enrolled CAPM, CAROM, and COVID-19 subjects without mucormycosis (controls; age-matched). We collected information on demography, predisposing factors, and details of COVID-19 illness. Univariable analysis was used to compare CAPM and CAROM. We used multivariable logistic regression to evaluate factors associated with CAPM (with hypoxemia during COVID-19 as the primary exposure) and at 12-week mortality. RESULTS: We included 1724 cases (CAPM [n = 122], CAROM [n = 1602]) and 3911 controls. Male sex, renal transplantation, multimorbidity, neutrophil-lymphocyte ratio, intensive care admission, and cumulative glucocorticoid dose for COVID-19 were significantly higher in CAPM than in CAROM. On multivariable analysis, COVID-19-related hypoxemia (aOR, 2.384; 95% CI, 1.209-4.700), male sex, rural residence, diabetes mellitus, serum C-reactive protein, glucocorticoid, and zinc use during COVID-19 were independently associated with CAPM. CAPM reported a higher 12-week mortality than CAROM (56 of the 107 [52.3%] vs. 413 of the 1356 [30.5%]; p = 0.0001). Hypoxemia during COVID-19 (aOR [95% CI], 3.70 [1.34-10.25]) and Aspergillus co-infection (aOR [95% CI], 5.40 [1.23-23.64]) were independently associated with mortality in CAPM, whereas surgery was associated with better survival. DISCUSSION: CAPM is a distinct entity with a higher mortality than CAROM. Hypoxemia during COVID-19 illness is associated with CAPM. COVID-19 hypoxemia and Aspergillus co-infection were associated with higher mortality in CAPM.

Robust Spatial-Spectral Squeeze-Excitation AdaBound Dense Network (SE-AB-Densenet) for Hyperspectral Image Classification.

Increasing importance in the field of artificial intelligence has led to huge progress in remote sensing. Deep learning approaches have made tremendous progress in hyperspectral image (HSI) classification. However, the complexity in classifying the HSI data using a common convolutional neural network is still a challenge. Further, the network architecture becomes more complex when different spatial-spectral feature information is extracted. Usually, CNN has a large number of trainable parameters, which increases the computational complexity of HSI data. In this paper, an optimized squeeze-excitation AdaBound dense network (SE-AB-DenseNet) is designed to emphasize the significant spatial-spectral features of HSI data. The dense network is combined with the AdaBound and squeeze-excitation modules to give lower computation costs and better classification performance. The AdaBound optimizer gives the proposed model the ability to improve its stability and enhance its classification accuracy by approximately 2%. Additionally, the cutout regularization technique is used for HSI spatial-spectral classification to overcome the problem of overfitting. The experiments were carried out on two commonly used hyperspectral datasets (Indian Pines and Salinas). The experiment results on the datasets show a competitive classification accuracy when compared with state-of-the-art methods with limited training samples. From the SE-AB-DenseNet with the cutout model, the overall accuracies for the Indian Pines and Salinas datasets were observed to be 99.37 and 99.78, respectively.

Effect of a High-Protein High-Fibre Nutritional Supplement on Lipid Profile in Overweight/Obese Adults with Type 2 Diabetes Mellitus: A 24-Week Randomized Controlled Trial.

BACKGROUND: Foods rich in protein and dietary fibre could potentially improve lipid profile in overweight or obese diabetic patients with dyslipidemia and, thereby, mitigate their risk of cardiovascular disease (CVD). In this study, the effect of providing high-protein high-fibre (HPHF) nutritional supplement in addition to standard care of type 2 diabetes mellitus (T2DM) on lipid profile was evaluated. METHODS: In this open-label, parallel-arm, prospective, randomized study, a total of 100 overweight/obese participants with T2DM were randomized to either an intervention group (25 g HPHF nutritional supplement given twice daily along with a standard care of T2DM) or a control group (standard care of T2DM) for 24 weeks. Change from baseline in lipid parameters such as total cholesterol (TChol), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) was assessed between the intervention and control group at week 12 and week 24. Participant compliance was assessed using the dietary 24-hour recall. Statistical analysis was performed to assess the main effects on within- and between-group changes from baseline to end of 24 weeks. RESULTS: Participants in the HPHF nutritional supplement group showed a statistically significant improvement in HDL-C levels by the end of 24 weeks (p=0.04) and a significant increase in protein and total dietary fibre intake (p=0.002 and p=0.00, respectively) compared to the control group. The TChol/HDL-C ratio was significantly lower (p=0.03) in the HPHF group from baseline to 24 weeks. CONCLUSION: Twice-daily consumption of a HPHF nutritional supplement significantly improved HDL-C levels. Inclusion of the HPHF supplement would be a useful effective aid for managing dyslipidemia in overweight/obese individuals with T2DM.

Glycemic Index and Microstructure Evaluation of Four Cereal Grain Foods.

To determine the glycemic index (GI) of selected cereals and association with their microstructure. The GI of whole grain pilaf (WGP), instant brown rice (IBR), whole maize ugali (MWU), and refined maize ugali (RMU) was assessed in a randomized trial. Fourteen healthy participants with mean age of 25 years were administered 50 g portions of available carbohydrates from glucose and various test foods after an overnight fast on separate occasions. Capillary blood samples of participants were used to measure blood glucose over 2 hr. The GI was calculated as per standard protocol. The microstructure of test foods, determined by scanning electron microscopy was evaluated to understand the measured GI values. The GI (mean ± standard error) of IBR was the highest (87.8 ± 6.8) followed by RMU (74.7 ± 6.5) and WMU (71.4 ± 5.1). WGP had medium GI (58.9 ± 5.1; P < 0.01 vs. IBR). Microstructure examination of IBR revealed disruption of bran layer and presence of fissures indicating loss of intactness of bran. Stereozoom images for WGP revealed intact bran and germ. For RMU and WMU, the grain was milled leading to loss of integrity. IBR, RMU, and WMU have high GI values, which is likely due to disruption of bran layer, endosperm modification (IBR), and loss of grain matrix (WMU, RMU). WGP has medium GI probably due to fairly intact bran and germ. PRACTICAL APPLICATION: Wholegrain or whole meal flour may not necessarily be low in glycemic index (GI; low GI < 55; medium 55 to 69 and high GI ≥70). "Ugali" a commonly consumed cereal staple food in Tanzania made from either refined or whole meal maize flour was found to be a high GI food. Intact whole grain foods, such as whole grain pilaf (mixed intact whole grains) is a healthier alternative to milled whole grains such as whole meal maize flour. Instant quick cooking brown rice exhibited a high GI, due to the processing method, suggesting that regular brown rice may be a healthier option.

Dietary fat intake and its association with risk of selected components of the metabolic syndrome among rural South Indians.

CONTEXT: There is limited literature on the dietary fat intake of rural Indian populations, particularly in relation to the risk of metabolic syndrome (MS). AIM: This study aims to assess the dietary fat intake and analyze its association with the risk of selected components of the MS among rural population in the state of Tamil Nadu. SETTINGS AND DESIGN: Adults (n = 27012) ≥20 years of age were recruited from the rural component of the Chennai Urban Rural Epidemiological Study, a cross-sectional study conducted in 42 villages in Kanchipuram District of Tamil Nadu. SUBJECTS AND METHODS: Using a validated food frequency questionnaire, data were obtained on the fat intake among 6907 adults. Anthropometric and clinical measures were collected using standard methods. The components of the MS assessed were abdominal obesity, hypertension, and impaired fasting glucose. All analyses were performed using SPSS software (version 20). RESULTS: Prevalence of abdominal obesity, hypertension, and impaired fasting glucose were significantly higher in the highest quintile of fat intake (33%, P < 0.001; 39%, P = 0.04, and 23.3%, P = 0.003, respectively). Highest intake of fat was also significantly associated with risk of abdominal obesity (P < 0.001), hypertension (P = 0.04), and impaired fasting glucose (P = 0.01). Sunflower oil as the main cooking oil was significantly associated with a higher risk of these components of the MS (P for trend <0.001) compared to traditional oils and palmolein. CONCLUSIONS: Higher dietary fat was significantly associated with risk of components of the MS and use of sunflower oil as main cooking oil increased metabolic risk in rural South Indians.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification.

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database.

Personal Authentication Using Multifeatures Multispectral Palm Print Traits.

Biometrics authentication is an effective method for automatically recognizing a person's identity with high confidence. Multispectral palm print biometric system is relatively new biometric technology and is in the progression of being endlessly refined and developed. Multispectral palm print biometric system is a promising biometric technology for use in various applications including banking solutions, access control, hospital, construction, and forensic applications. This paper proposes a multispectral palm print recognition method with extraction of multiple features using kernel principal component analysis and modified finite radon transform. Finally, the images are classified using Local Mean K-Nearest Centroid Neighbor algorithm. The proposed method efficiently accommodates the rotational, potential deformations and translational changes by encoding the orientation conserving features. The proposed system analyses the hand vascular authentication using two databases acquired with touch-based and contactless imaging setup collected from multispectral Poly U palm print database and CASIA database. The experimental results clearly demonstrate that the proposed multispectral palm print authentication obtained better result compared to other methods discussed in the literature.