Novel Information Measures for Fermatean Fuzzy Sets and Their Applications to Pattern Recognition and Medical Diagnosis.

Fermatean fuzzy sets (FFSs) have piqued the interest of researchers in a wide range of domains. The striking framework of the FFS is keen to provide the larger preference domain for the modeling of ambiguous information deploying the degrees of membership and nonmembership. Furthermore, FFSs prevail over the theories of intuitionistic fuzzy sets and Pythagorean fuzzy sets owing to their broader space, adjustable parameter, flexible structure, and influential design. The information measures, being a significant part of the literature, are crucial and beneficial tools that are widely applied in decision-making, data mining, medical diagnosis, and pattern recognition. This paper aims to expand the literature on FFSs by proposing many innovative Fermatean fuzzy sets-based information measures, namely, distance measure, similarity measure, entropy measure, and inclusion measure. We investigate the relationship between distance, similarity, entropy, and inclusion measures for FFSs. Another achievement of this research is to establish a systematic transformation of information measures (distance measure, similarity measure, entropy measure, and inclusion measure) for the FFSs. To accomplish this aim, new formulae for information measures of FFSs have been presented. To demonstrate the validity of the measures, we employ them in pattern recognition, building materials, and medical diagnosis. Additionally, a comparison between traditional and novel similarity measures is described in terms of counter-intuitive cases. The findings demonstrate that the innovative information measures do not include any absurd cases.

Automatic detection of COVID-19 from chest radiographs using deep learning.

INTRODUCTION: The breakdown of a deadly infectious disease caused by a newly discovered coronavirus (named SARS n-CoV2) back in December 2019 has shown no respite to slow or stop in general. This contagious disease has spread across different lengths and breadths of the globe, taking a death toll to nearly 700 k by the start of August 2020. The number is well expected to rise even more significantly. In the absence of a thoroughly tested and approved vaccine, the onus primarily lies on obliging to standard operating procedures and timely detection and isolation of the infected persons. The detection of SARS n-CoV2 has been one of the core concerns during the fight against this pandemic. To keep up with the scale of the outbreak, testing needs to be scaled at par with it. With the conventional PCR testing, most of the countries have struggled to minimize the gap between the scale of outbreak and scale of testing. METHOD: One way of expediting the scale of testing is to shift to a rigorous computational model driven by deep neural networks, as proposed here in this paper. The proposed model is a non-contact process of determining whether a subject is infected or not and is achieved by using chest radiographs; one of the most widely used imaging technique for clinical diagnosis due to fast imaging and low cost. The dataset used in this work contains 1428 chest radiographs with confirmed COVID-19 positive, common bacterial pneumonia, and healthy cases (no infection). We explored the pre-trained VGG-16 model for classification tasks in this. Transfer learning with fine-tuning was used in this study to train the network on relatively small chest radiographs effectively. RESULTS: Initial experiments showed that the model achieved promising results and can be significantly used to expedite COVID-19 detection. The experimentation showed an accuracy of 96% and 92.5% in two and three output class cases, respectively. CONCLUSION: We believe that this study could be used as an initial screening, which can help healthcare professionals to treat the COVID patients by timely detecting better and screening the presence of disease. IMPLICATION FOR PRACTICE: Its simplicity drives the proposed deep neural network model, the capability to work on small image dataset, the non-contact method with acceptable accuracy is a potential alternative for rapid COVID-19 testing that can be adapted by the medical fraternity considering the criticality of the time along with the magnitudes of the outbreak.

Free radical induced increase in protein carbonyl is attenuated by low dose of adenosine in hippocampus and mid brain: implication in neurodegenerative disorders.

There is strong evidence that oxidative stress participates in the etiology of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. In the previous studies we have already shown that a combination of alpha-tocopherol and ascorbic acid protect neurons against tert-butyl hydroperoxide (t-BuOOH) induced neurotoxicity in different brain regions including hippocampus and mid brain. In this work, we examined the neuroprotective effect of low dose of adenosine against protein oxidation (protein carbonyls) in parallel with the level of reduced glutathione (GSH) in hippocampus and mid brain regions of mouse brain. The t-BuOOH was injected intraperitoneally in three concentrations (50, 100, 150 mg/kg b.w.) for 10 days. Results showed dose dependent increase in protein carbonyl (PC) in hippocampus and mid brain region. This increase was accompanied by a significant (p < 0.05) decline in GSH content in both brain regions of t-BuOOH treated mice. Adenosine (1 mg/kg b.w.) protected both hippocampus and mid brain neurons against protein oxidation as evidenced by reduction in protein carbonyl content. The GSH content was significantly (p < 0.05) increased after the treatment of adenosine in both brain regions. These data show that prior treatment with low dose of adenosine attenuates the oxidative protein damage with parallel increase in the GSH level in hippocampus and mid brain of t-BuOOH induced mice.

Multivariate analysis of factors associated with postoperative pulmonary complications following general elective surgery.

OBJECTIVE: To develop a predictive model identifying perioperative conditions associated with postoperative pulmonary complications (PPCs). DESIGN: A prospective survey of patients whose preoperative history and physical examination, spirometric, PaO2 and PaCO2 analysis, and operative results were recorded. These patients underwent postoperative cardiopulmonary examinations until they were discharged from the hospital; their medical records were also reviewed until they were discharged from the hospital. SETTING: The Louisville Veterans Administration Medical Center, Louisville, Ky. PATIENTS: A randomly chosen sample of patients aged 40 years or older who required elective, nonthoracic surgery under general or spinal anesthesia and who were hospitalized at least 24 hours postoperatively. MAIN OUTCOME MEASURE: An analysis of risk factors associated with the development of 1 or more of the following conditions: acute bronchitis, bronchospasm, atelectasis, pneumonia, adult respiratory distress syndrome, pleural effusion, pneumothorax, prolonged mechanical ventilation, or death secondary to acute respiratory failure. RESULTS: Postoperative pulmonary complications developed in 16 (11%) of 148 patients. The risk factors found to be higher among those with PPCs compared with those without PPCs were postoperative nasogastric intubation (81% vs 16%, P<.001), preoperative sputum production (56% vs 21%, P=.005), and longer anesthesia duration (480 vs 309 minutes, P<.001). Upper abdominal surgery was performed in 11 (69%) of the 16 patients with PPCs and in 20 (15%) of the 132 patients without PPCs (P<.001); this difference lost significance in multivariate analysis. The final linear logistic model included postoperative nasogastric intubation (odds ratio [OR], 21.8), preoperative sputum production (OR, 4.6), and longer anesthesia duration (OR exp[0.01x] for an increase in x minutes) (1 minute of additional anesthesia time increases the OR to 1.01), resulting in 92% accuracy in predicting PPCs. CONCLUSIONS: We identified 3 potentially modifiable risk factors for PPCs. If validated, our results may lead to modifications of perioperative care that will further reduce PPCs.

Drug-induced disorders of glucose tolerance.

PURPOSE: To review the medications that influence glucose metabolism and to examine the mechanisms of these medications on glucose metabolism. DATA SOURCES: Data were obtained from a MEDLINE search back to 1966 and included animal and human studies published in the English language. STUDY SELECTION: Approximately 80% of original publications were included after review by the authors. Case reports were included if they provided additional information. DATE EXTRACTION: The original data from the literature were included on the basis of independent extraction by the authors. DATA SYNTHESIS: Many common therapeutic agents influence glucose metabolism. Multiple mechanisms of action on glucose metabolism exist through pancreatic, hepatic, and peripheral effects. Based on circumstances at the time of use, a drug may cause both hyper- and hypoglycemia in a patient. The patient's previous pancreatic reserve, nutritional state, use of other medication, or exposure to alcohol may influence the direction of the plasma glucose alterations. CONCLUSION: Hyperinsulinemia and insulin resistance form an intrinsic component of diabetes, hyperlipidemia, and atherosclerotic vascular disease (syndrome X). The induction of hyperinsulinemia and insulin resistance by medication may therefore counteract intended benefits. An extensive review of recent medication in patients with disorders of glucose tolerance and the avoidance of polypharmacy are recommended. It is prudent to monitor plasma glucose values when it is not possible to avoid prescription of medication with known effects on carbohydrate metabolism.

Efficacy of serum lactate dehydrogenase and its isozymes in monitoring the therapy in patients with acute leukemia.

The pretreatment levels of serum Lactate Dehydrogenase (LDH) and its isozymes are measured in 12 cases of Acute Lymphoid Leukemia (ALL) and 8 cases of Acute Myeloid Leukemia (AML) and compared with 14 matched healthy controls. Patients showed only first three bands of LDH while normal sera exhibited five bands. A significant increase in total LDH activity was observed in patients of both the groups as compared to controls at the time of admission. The patients were treated with chemotherapy and were followed at 72 hrs. and after 4 weeks. The patients who successfully responded to the therapy showed increased levels of total LDH and LDH2 and LDH3 isoenzymes as early as 72 hrs. after commencement of the therapy. The nonresponders, on the other hand, circulated, decreased or unaltered values of isozymes during this interval. Following them up for a month, ALL responders showed decreased values of total LDH activity, LDH2 and LDH3 activities when compared with their 72 hour values except LDH1. Non responders of this group had practically unaltered values of these isozymes. AML responders circulated decreased values of all the isozymes while AML non responders "showed significant increases in total LDH, and all its isozymes as compared with their 72 hour values. The determination of total enzyme and its isozyme levels at pre, mid and end of treatment seems to be a promising biochemical parameter to predict the early response to chemotherapy administered.