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      Mathematical model and artificial intelligence for diagnosis of Alzheimer’s disease

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          Abstract

          Degeneration of the neurological system linked to cognitive deficits, daily living exercise clutters, and behavioral disturbing impacts may define Alzheimer’s disease. Alzheimer’s disease research conducted later in life focuses on describing ways for early detection of dementia, a kind of mental disorder. To tailor our care to each patient, we utilized visual cues to determine how they were feeling. We did this by outlining two approaches to diagnosing a person’s mental health. Support vector machine is the first technique. Image characteristics are extracted using a fractal model for classification in this method. With this technique, the histogram of a picture is modeled after a Gaussian distribution. Classification was performed with several support vector machines kernels, and the outcomes were compared. Step two proposes using a deep convolutional neural network architecture to identify Alzheimer’s-related mental disorders. According to the findings, the support vector machines approach accurately recognized over 93% of the photos tested. The deep convolutional neural network approach was one hundred percent accurate during model training, whereas the support vector machines approach achieved just 93 percent accuracy. In contrast to support vector machines accuracy of 89.3%, the deep convolutional neural network model test findings were accurate 98.8% of the time. Based on the findings reported here, the proposed deep convolutional neural network architecture may be used for diagnostic purposes involving the patient’s mental state.

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          Artificial intelligence in healthcare

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            Critical care crisis and some recommendations during the COVID-19 epidemic in China

            Since December 2019, a severe acute respiratory infection (SARI) caused by 2019 novel coronavirus (SARS-CoV-2), began to spread from Wuhan to all of China [1, 2], and indeed the world. As of Feb 10, 2020, there are more than 40,000 confirmed cases and > 1000 deaths in China. Lack of critical care resource in face of COVID-19 epidemics Based on data reported by the National Health Commission of China, there have been about 2000 new confirmed cases and > 4000 suspected cases daily over the past week in Wuhan [3]. About 15% of the patients have developed severe pneumonia, and about 6% need noninvasive or invasive ventilatory support. Currently, there are about 1000 patients who need ventilatory support and another 120 new patients daily who require noninvasive or invasive ventilation support in Wuhan city; however, there are only about 600 ICU beds [4]. To address this shortfall, 70 ICU beds were created from general beds and the government quickly transformed three general hospitals to critical care hospitals with a total of about 2500 beds that specialize in patients with severe SARS-CoV-2 pneumonia (equipped with monitors and high-flow nasal cannula, noninvasive ventilator or invasive ventilators). An equally great (or potentially greater) problem is the shortage of trained personnel to treat these critically ill patients. Until the crisis, there were about 300 ICU physicians and 1000 ICU nurses in Wuhan city. By the end of January, more than 600 additional ICU doctors and 1500 ICU nurses were transferred to Wuhan from the rest of China. As well, an additional 3000 staff including infectious disease, respiratory, internal medicine physicians and nurses were transferred to Wuhan by the government. There are logistical issues which make care of the patients difficult. These include donning of personal protective equipment (e.g., gloves, gowns, respiratory and eye protection), lack of instruments and disposables, and shortages of supplemental oxygen. Many severe hypoxemic patients only receive high-flow nasal oxygen (HFNO) or noninvasive mechanical ventilation rather than invasive mechanical ventilation because of intubation delay or lack of mechanical ventilators (especially at early phase). Our preliminary data show that only about 25% of patients who died were intubated and received mechanical ventilation. Recommendations It’s not possible at this stage to create new equipment or personnel. However, it would be very helpful to have mathematical models developed which predict the expected number of patients, and the necessary resources (equipment and personnel) required to treat these patients. This would aid in determining what resources might be moved to Wuhan to help local health care personnel. Challenge of early recognition and treatment of critical SARI patients Several previous reports have described the characteristics of SARS-CoV-2 infected patients [2, 5, 6]. Most patients are > 50 years of age; the mean age is much older than patients infected with H1N1 or with Middle East respiratory syndrome (MERS) [7–9]. About 30 to 50% of COVID-19 patients have chronic comorbidities. The duration from the initial symptom to respiratory failure in most patients is > 7 days, which is longer than H1N1 [7, 8]. Additionally, many patients that go on to develop respiratory failure had hypoxemia but without signs of respiratory distress, especially in the elderly patients (“silent hypoxemia”). Moreover, only a very small proportion of patients have other organ dysfunction (e.g., shock, acute kidney injury) prior to developing respiratory failure. These characteristics suggest that traditional methods such as quick sequential organ failure assessment (qSOFA) score and the new early warning score (NEWS) may not help predict those patients who will go on to develop respiratory failure. Therefore, it is urgent to establish a prediction or early recognition model of patients likely to fail. Although the novel coronavirus was quickly isolated and sequenced [10], there are no proven, effective drugs to treat COVID-19. Based on in vitro screening studies, several drugs were found to inhibit the virus [11]. One case report demonstrated a surprising effect of remdesivir for SARS-CoV-2 infection [12]; however, the clinical impact remains unclear. Encouragingly, several clinical trials are undergoing (ChiCTR2000029308, NCT04252664 and NCT04257656) to determine the effect of lopinavir/ritonavir or remdesivir. We have also tried Traditional Chinese Medicine such as Xuebijing, and several clinical trials are ongoing in this regard. Recommendations Identifying a biomarker(s) that predicts severity and outcome in COVID-19 patients early in the presentation would be extremely helpful. Our data (unpublished) demonstrate that severe lymphopenia and high levels of C-reactive protein correlated with the severity of hypoxemia and predicted hospital mortality. In addition, the change of lymphocyte counts during the first 4 days after hospital admission was highly associated with mortality. Crisis in management of SARI in the ICU The mortality rate of SARI is highest (4%) in Wuhan city, followed by other cities in Hubei province (1.4%) and other provinces (0.25%) [3]. The higher morality in Wuhan may due to the limited resources, but we are uncertain whether patients are sicker in Wuhan than in other cities. Understanding the characteristics of the dead patients would help in triaging patients and allocating resources. We analyzed data of 135 patients who died before Jan 30, 2019, in Wuhan city. Older age and male were common in non-surviving patients. More than 70% patients had one or more comorbidities. Hypertension (48.2%) was the most common comorbidity in non-surviving patients, followed by diabetes (26.7%) and ischemic heart disease (17.0%), similar to data reported by others [5, 6]. Importantly, as stated above, of the patients who died only ~ 25% received invasive mechanical ventilation or ECMO. The median duration of HFNO and/or NIV was 6(4–8) days before intubation or death. The mortality of patients who received ECMO is high: of 28 patients who received ECMO up to the present, 14 died, 5 weaned successfully, and 9 are still on ECMO. Lack of ventilators, fear of becoming infected during the intubation procedure, and unclear need for intubation were the main reasons for delaying invasive ventilation. Compliance with lung protective ventilation strategy is also low in some centers, with some patients receiving tidal volumes > 8 ml/kg PBW and with high driving pressures. Sedation and paralysis strategies are also not standardized. Lack of intensivists may be a potential cause. Fortunately, we found a significant benefit of prone position in most severe ARDS patients. Recommendations There should be a focus on high-risk patients, e.g., male, > 60 years old, and patients with comorbidities. Additionally, a standard protocol for SARS-CoV-2 infection recommended by World Health Organization should be widely implemented [13]. It is crucial that our staff is trained to employ standard protocols for management, which may help implement evidence-based ventilatory and general ICU care in the face of an overwhelming workload. More importantly, in the context of a multidisciplinary team, intensivists should act as leaders, ensuring that severe patients receive standardized treatment (Fig. 1). Fig. 1 Some recommendations to face the critical care crisis due to the COVID-19 epidemic In summary, the COVID-19 epidemic has placed a huge burden on the Chinese health care system. This crisis has dramatically affected the delivery of critical care due to a lack of resources, lack of prediction models and of course the lack of effective pharmacotherapies. Front line critical care clinicians desperately require these tools.
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              Investigation of effective climatology parameters on COVID-19 outbreak in Iran

              SARS CoV-2 (COVID-19) Coronavirus cases are confirmed throughout the world and millions of people are being put into quarantine. A better understanding of the effective parameters in infection spreading can bring about a logical measurement toward COVID-19. The effect of climatic factors on spreading of COVID-19 can play an important role in the new Coronavirus outbreak. In this study, the main parameters, including the number of infected people with COVID-19, population density, intra-provincial movement, and infection days to end of the study period, average temperature, average precipitation, humidity, wind speed, and average solar radiation investigated to understand how can these parameters effects on COVID-19 spreading in Iran? The Partial correlation coefficient (PCC) and Sobol’-Jansen methods are used for analyzing the effect and correlation of variables with the COVID-19 spreading rate. The result of sensitivity analysis shows that the population density, intra-provincial movement have a direct relationship with the infection outbreak. Conversely, areas with low values of wind speed, humidity, and solar radiation exposure to a high rate of infection that support the virus's survival. The provinces such as Tehran, Mazandaran, Alborz, Gilan, and Qom are more susceptible to infection because of high population density, intra-provincial movements and high humidity rate in comparison with Southern provinces.
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                Author and article information

                Contributors
                A.davodabadi66@yahoo.com
                Be.daneshian@iauctb.ac.ir
                s_saatim@iau-tnb.ac.ir
                Sh_razavyan@azad.ac.ir
                Journal
                Eur Phys J Plus
                Eur Phys J Plus
                European Physical Journal plus
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                2190-5444
                29 May 2023
                2023
                : 138
                : 5
                : 474
                Affiliations
                [1 ]GRID grid.411463.5, ISNI 0000 0001 0706 2472, Department of Mathematics, Central Tehran Branch, , Islamic Azad University, ; Tehran, Iran
                [2 ]GRID grid.411463.5, ISNI 0000 0001 0706 2472, Department of Mathematics, North Tehran Branch, , Islamic Azad University, ; Tehran, Iran
                [3 ]GRID grid.411463.5, ISNI 0000 0001 0706 2472, Department of Mathematics, South Tehran Branch, , Islamic Azad University, ; Tehran, Iran
                Article
                4128
                10.1140/epjp/s13360-023-04128-5
                10226030
                37274456
                c8ed5c3f-5486-446f-a39f-8faa4661199c
                © The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 31 March 2023
                : 22 May 2023
                Categories
                Regular Article
                Custom metadata
                © Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023

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