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Introduction: N95 Filtering Face-piece Respirators (FFRs) prevent the spread of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection and protect medical personnel. It is considered as one of the essential protective equipment. An increased demand for N95 FFRs during the Coronavirus Disease 2019 (COVID-19) pandemic has resulted in shortage crisis for the care of cases arising in hospitals. Therefore, this situation generated the need to implement alternatives like four mask policy that allows reuse of N95 FFRs in settings with limited resources. Aim: To find out the burden of bacterial isolates on N95 FFRs after reuse. Materials and Methods: The present study was a prospective study conducted at Govt. R.D.B.P. Jaipuria hospital, Jaipur, Rajasthan, India, from May 2021 to October 2021 on 526 used N95 FFRs after multiple or extended use by 96 hospital personnel who attended Outpatient Department (OPD) services. Swab samples were collected from both external as well as internal surface of the mask after each use and further subcultured to identify any microorganism present on them. Results: Growth of Gram positive bacilli, and micrococci were found on 499 samples out of total 526 samples. Other bacteria like Pseudomonas spp. and other gram negative were also observed in 19 samples. Furthermore, it was also observed that bacterial load on reused N95 FFRs was mainly commensal flora. Conclusion: Mainly commensal floras were found over internal and external surface of used N95 FFRs. This proves that four mask policy is optimum for judicious use of N95 FFRs and resulted in saving of funds for other more emergency works mainly in OPD settings. [ABSTRACT FROM AUTHOR]

Feature selection is an important component of the machine learning domain, which selects the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a given number of features, there are 2n possible feature subsets, making it challenging to select the optimal set of features from a dataset via conventional feature selection approaches. We opted to investigate glaucoma infection since the number of individuals with this disease is rising quickly around the world. The goal of this study is to use the feature set (features derived from fundus images of benchmark datasets) to classify images into two classes (infected and normal) and to select the fewest features (feature selection) to achieve the best performance on various efficiency measuring metrics. In light of this, the paper implements and recommends a metaheuristics-based technique for feature selection based on emperor penguin optimization, bacterial foraging optimization, and proposes their hybrid algorithm. From the retinal fundus benchmark images, a total of 36 features were extracted. The proposed technique for selecting features minimizes the number of features while improving classification accuracy. Six machine learning classifiers classify on the basis of a smaller subset of features provided by these three optimization techniques. In addition to the execution time, eight statistically based performance metrics are calculated. The hybrid optimization technique combined with random forest achieves the highest accuracy, up to 0.95410. Because the proposed medical decision support system is effective and ensures trustworthy decision-making for glaucoma screening, it might be utilized by medical practitioners as a second opinion tool, as well as assist overworked expert ophthalmologists and prevent individuals from losing their eyesight. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel feature selection approach for breast cancer prediction is proposed. • Feature selection was based on the three soft-computing algorithms. • The Bacterial foraging algorithm and the Emperor penguin algorithm are implemented. • Hybrid of Bacterial foraging algorithm and Emperor penguin algorithm is proposed. • Nine performance metrics are computed for six machine learning classifiers. Feature selection is one of the crucial data preprocessing techniques for improving the performance of machine learning (ML) models. Recently, metaheuristic feature selection algorithms have become popular because they select optimal features for ML problems. This paper presents three feature selection strategies based on metaheuristic algorithms: Bacterial Foraging (BFOA), Emperor Penguin (EPO), and a hybrid (hBFEPO) combining BFOA and EPO. The baseline algorithms have been investigated for feature selection in other ML tasks, but not for breast cancer classification. A hybrid of these two has been used for the first time. These strategies were initially tested on the COVID-19 dataset. After achieving satisfactory results, these strategies are evaluated on the WDBC Breast Cancer dataset. The performance of our models on WDBC is compared with recent eighteen state-of-the-art studies. The results indicate that the hBFEPO model outperforms other models, achieving 100% precision and specificity, 98.49% accuracy, 95.43% sensitivity, a 95.99% F1-score, and a 99.60% AUC. [ABSTRACT FROM AUTHOR]

• A soft-computing and machine learning based medical decision support system is proposed for breast cancer disease classification. • Two soft-computing algorithms eagle strategy optimization (ESO) algorithm and the gravitational search optimization (GSO) algorithm are employed for the first time for this problem. A novel intelligent classification approach based on these two algorithms for breast cancer infection is also proposed. • 12 experiments have been performed using three soft-computing algorithms(for feature selection) and 6 state-of-the-art machine learning classifier(for classification).WDBC dataset is used for the method evaluation. • We have been able to attain astonishing results with accuracy up to 98.9578%, sensitivity up to 0.9696, specificity up to 1.000, precision 1.000, F1- score up to 0.9696, and an AUC 0.9980(close to maximum i.e., 1.0000). Feature selection, which picks the optimal subset of characteristics related to the target data by deleting unnecessary data, is one of the most important aspects of the machine learning area. A major part of big data preprocessing is feature selection (reduction). There are 2n alternative feature subsets for every n features, making it difficult to choose the best set of features from a dataset using typical feature selection techniques. Consequently, the present study proposes and suggests a unique feature selection method based on the Eagle Strategy(ESO) Optimization, Gravitational Search Optimization (GSO) algorithm, and their hybrid algorithm. We chose this infection as our subject of investigation since the number of women with breast cancer is increasing rapidly on a global scale. After lung cancer, which affects more women than any other kind of cancer, breast cancer is the second leading cause of cancer mortality. The goal of this study is to categorize breast cancer into two groups using the benchmark feature set (Wisconsin Diagnostic Breast Cancer (WDBC)) and to choose the fewest features (feature selection) to achieve maximum accuracy. This work also provides a hybrid technique for finding important features that combines two algorithms, ESO and the GSO algorithm, while reducing insignificant characteristics (features) and complexity. Soft computing technologies and machine learning algorithms provide a framework for prognostic research by classifying data instances as relevant or irrelevant depending on cancer severity. Thus, this work presented a new approach for classifying breast cancer tumors. In this research, we coupled soft computing methodologies—our implemented algorithms are applied for the first time to this problem—with artificial intelligence-based machine learning strategies to create a prediction model. The efficacy of our suggested technique was evaluated using WDBC breast cancer data sets, and the findings show that our proposed hybrid algorithm performs very well in breast cancer classification. We have been able to attain astonishing results with accuracy up to 98.9578%, sensitivity up to 0.9705, specificity up to 1.000, precision up to 1.000, F1-score up to 0.9696, and an AUC up to 0.9980 (close to maximum, i.e., 1.0000). Our study's goal is to incorporate our findings into a valid clinical prediction system, allowing visual science specialists to make more accurate and effective judgments in the future. Furthermore, our suggested technology might be used to detect a wide range of diseases. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a distinctive medical condition characterized by loss of memory, orientation, and cognitive impairments, which is an exceptionally universal form of neurodegenerative disease. The statistical data suggested that it is the 3rd major cause of death in older persons. Butyrylcholinesterase (BChE) and acetylcholinesterase (AChE) inhibitors play a vital role in the treatment of AD. Coumarins, natural derivatives, are reported as cholinesterase inhibitors and emerges as a promising scaffold for design of ligands targeting enzymes and pathological alterations related to AD. In this regard, the 3D QSAR pharmacophore models were developed for coumarin scaffold containing BChE and AChE inhibitors. Several 3D QSAR pharmacophore models were developed with FAST, BEST, and CEASER methods, and finally, statistically robust models (based on correlation coefficient, cost value, and RMSE value) were selected for further analysis for both targets. The important features ((HBA 1, HBA 2, HY, RA (BChE) HBA 1, HBA 2, HY, PI, (AChE)) were identified for good inhibitory activity of coumarin derivatives. Finally, the selected models were applied to various database compounds to find potential BChE and AChE inhibitors, and we found 13 for BChE and 1 potent compound for AChE with an estimated activity of IC50 < 10 µM. Further, the Lipinski filters, and ADMET analysis supports the selected compounds to become a drug candidate. These selected BChE and AChE inhibitors can be used in the treatment of AD. [ABSTRACT FROM AUTHOR]

In the case of communicable diseases, such as COVID-19, effective and quick testing techniques make it easier to identify a contaminated person, so that he or she can be easily isolated. To predict COVID-19-infected individuals through chest computed tomography scans, this study suggests an effective feature selection technique incorporated in clinical decision support system that may be used for testing. After pre-processing, we retrieved 213 features from the chest computed tomography images of a public data set with 2482 images. Then, in a two-step process, the most significant features for recognizing the difference between COVID patients and healthy individuals are selected. Initially, the Chi-square test selects 75% of the initial extracted features, which are then forwarded to three nature-inspired computing algorithms: the cuckoo search optimization algorithm, a teaching–learning-based optimization algorithm, and a hybrid of these two for further optimization. The finally selected reduced feature set and five machine learning classifiers are then employed to classify these computed tomography images. Twenty-four experiments using fivefold and tenfold cross-validation have been performed to find the best values for eight statistical efficiency evaluation metrics. Our suggested approach achieves a notable accuracy of 95.99%, the best mean intersection over union of 0.9655, and the highest area under curve of 0.9966. XGBoost delivers more effective, promising, and comparable results when compared to other ML classifiers. Our suggested testing approach will benefit frontline workers and the state by providing routine and cost-effective testing, and faster results. [ABSTRACT FROM AUTHOR]

• Wastewater-based surveillance has focused almost exclusively on urban areas. • Rural-urban public health surveillance and healthcare service disparities persist. • Recent research identifies pathways for implementing rural wastewater surveillance. The sampling and analysis of sewage for pathogens and other biomarkers offers a powerful tool for monitoring and understanding community health trends and potentially predicting disease outbreaks. Since the early months of the COVID-19 pandemic, the use of wastewater-based testing for public health surveillance has increased markedly. However, these efforts have focused on urban and peri‑urban areas. In most rural regions of the world, healthcare service access is more limited than in urban areas, and rural public health agencies typically have less disease outcome surveillance data than their urban counterparts. The potential public health benefits of wastewater-based surveillance for rural communities are therefore substantial – though so too are the methodological and ethical challenges. For many rural communities, population dynamics and insufficient, aging, and inadequately maintained wastewater collection and treatment infrastructure present obstacles to the reliable and responsible implementation of wastewater-based surveillance. Practitioner observations and research findings indicate that for many rural systems, typical implementation approaches for wastewater-based surveillance will not yield sufficiently reliable or actionable results. We discuss key challenges and potential strategies to address them. However, to support and expand the implementation of responsible, reliable, and ethical wastewater-based surveillance for rural communities, best practice guidelines and standards are needed. [Display omitted] [ABSTRACT FROM AUTHOR]

Loss/alteration of Smell and taste sensation is common in CoViD-19 infection. We conducted present study to find out the frequency, onset and severity of these lost sensations and their recovery in mild, moderate and severe COVID 19 positive patients in our setup. A questionnaire based study on 574 COVID-19 positive patients admitted in a dedicated COVID hospital between September–November, 2020 were followed up until their sensations recovered completely or maximum for two months. Fever was the most common symptom reported. Loss of smell and taste sensation is seen in 200 (34.84%) and 269 (46.86%) patients respectively; 163 (28.4%) developed both. Males were affected significantly more than females (p = 0.030 and 0.027). Approximately 1/4th patients [49 (24.5%) and 55 (20.45%)] reported loss of smell and taste sensation as their first symptom. Most common taste sensation lost was salty 191 (71.0%). Loss of smell sensation is seen maximally in mild cases and the difference among mild, moderate and severe cases is statistically significant (p = 0.00001); while the difference in loss of taste among all three grades of severity is statistically insignificant (p = 0.0770). Most of the patients [smell (142; 71%) and taste (198; 73.6%)] recovered after 2 weeks of onset of lost sensations while 96.5% (193/200) and 98.1% (264/269) patients reported complete recovery of smell and taste sensations after two months of onset. Present study shows that high percentage of COVID-19 positive patients develop loss of either one or both of smell and taste sensations but recovery is fast and complete in most of them. [ABSTRACT FROM AUTHOR]

• A feature selection strategy which selects the optimal subset of features, by employing GSOA, from the original set is developed, in which the elimination of extraneous features takes place which enhances the classification performance and reduces the computational cost. This improves glaucoma detection while lowering model training computational costs and execution time. The performance is evaluated on the customized dataset of public and private images. • Further in this research, we discuss the application of machine learning in anticipating diseases,thus expanding the field of medical services and bringing in the most recent technology. It is demonstrated how to use the suggested technique, and a comprehensive analysis of different parameters is given. To determine the most effective presentation, various efficiency assessment criteria for many machine learning classifiers were implemented. The parameters included are accuracy, sensitivity,specificity, ROC curves and many more. • Through this study, we offer the best features(selected through soft-computing approach) to researchers, a reliable and effective system of support for medical professionals, and a software-based tool for the human race to slow down this infection spreading through early, quick, and accurate identification of this infection. It might be used in locations with a shortage of qualified medical workers like doctors and nurses. It can also be altered to work with wearable and portable medical devices. • For the detection of this infection from retinal fundus images, our proposed method outperforms existing techniques. This testing procedure will be advantageous for doctors and the state because it is significantly less expensive than other diagnostic instruments used by medical professionals to detect the disease. In addition, owing to its high performance, the proposed medical prediction system can be used to develop mobile applications that aid physicians in the early diagnosis of glaucoma. The process of feature selection (FS) is vital aspect of machine learning (ML) model's performance enhancement where the objective is the selection of the most influential subset of features. This paper suggests the Gravitational search optimization algorithm (GSOA) technique for metaheuristic-based FS. Glaucoma disease is selected as the subject of investigation as this disease is spreading worldwide at a very fast pace; 111 million instances of glaucoma are expected by 2040, up from 64 million in 2015. It causes widespread vision impairment. Optic nerve fibres can be degraded and cannot be replaced later in this disease. As a starting point, the retinal fundus images of glaucoma infected persons and healthy persons are used, and 36 features were retrieved from these images of public benchmark datasets and private dataset. Six ML models are trained for classification on the basis of the GSOA's returned subset of features. The suggested FS technique enhances classification performance with selection of most influential features. The eight statistical performance evaluating parameters along with execution time are calculated. The training and testing have been performed using a split approach (70:30), 5-fold cross validation (CV), as well as 10-fold CV. The suggested approach achieved 95.36 % accuracy. Due to its auspicious performance, doctors might use the suggested method to receive a second opinion, which would also help overburdened skilled medical practitioners and save patients from vision loss. [ABSTRACT FROM AUTHOR]

Hereditary hearing loss accounts for nearly 60% of deafness in developed countries and about 30% of them are syndromic. Pierre Robin Syndrome is one such condition. The patient with this syndrome usually presnts with triad of micrognathia, glossoptosis and cleft palate. Hearing loss is mostly conductive but there can be sensorineural hearing loss also. Here we present a case of Pierre Robin Syndrome who presented with congenital hearing loss. He also had bilateral serous otitis media. He underwent cochlear implant surgery and was prescribed antihistaminics and steroid spray for middle ear effusion. Therefore, proper clinical evaluation is required. [ABSTRACT FROM AUTHOR]

• We 65 extracted structural and non-structural feature sets for glaucoma prediction. • Our selected three nature-computing algorithms and two novel algorithms have been applied first time to these glaucoma datasets in combination with these characteristics. As a result, the results from this series of experiments are entirely novel. • The performance test of Bat Algorithm and Binary Cuckoo Search on two typical publicly accessible datasets demonstrates that they are highly efficient in glaucoma prediction. Extensive testing and analysis were conducted (3 machine learning and 5 soft computing techniques were tested on three combinations of two datasets). • This work adds to scientific research by suggesting a unique classifier strategy that achieves astonishing results, up to 98.95% accuracy in detecting glaucoma illness in the binary class. The suggested system's categorization accuracy is evaluated against two widely used and publicly accessible datasets. • This work provides an automated primary glaucoma screening system based on quantitative analysis of fundus pictures to aid ophthalmologists in detecting glaucoma disease more quickly and affordably. It could also be used as a second opinion. With this system, human race has been equipped with a low-cost, rapid, robust, readily implementable, and reliable intelligence system for screening this infection. As a result, the pace of eyesight loss may be minimized. Glaucoma is the second most common cause of vision loss. Manual screening of a patient's eye or screening through a fundus image of the patient's eye requires expert ophthalmologists. This screening analysis is time-consuming, requires expert human involvement, and is subject to human intra-observer variability. Thus, medical imaging professionals are working to solve these issues by investigating retinal images for glaucoma detection using artificial intelligence-based computer-aided diagnosis systems (CAD). Machine learning algorithms (for classification) and nature-inspired computing (for feature selection/reduction) embedded CAD systems can successfully identify retinal pictures and can be employed to overcome these challenges. This proposed work is a productive attempt in which we have proposed two novel two-layered approaches (BA-BCS, BCS-PSO) which are based on Particle Swarm Optimization (PSO), Binary Cuckoo Search (BCS), and Bat Algorithm (BA). We have also analyzed the performances of BA, BCS, and PSO separately. These five (single and two-layered) approaches are used to compose subsets of reduced features that can generate the maximum accuracy when forwarded to three machine learning classifiers. Benchmark publicly available datasets, ORIGA and REFUGE, and their combinations are used to validate the proposed methodology. A maximum accuracy of up to 98.95% is achieved using these approaches. Apart from this, many other trade-off solutions are also suggested for the researcher's community. This study therefore presents novel efforts with new and efficient results that are beneficial to ophthalmologists, researchers, and humanity. [ABSTRACT FROM AUTHOR]