Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings.

Accurate screening of COVID-19 infection status for symptomatic patients is a critical public health task. Although molecular and antigen tests now exist for COVID-19, in resource-limited settings, screening tests are often not available. Furthermore, during the early stages of the pandemic tests were not available in any capacity. We utilized an automated machine learning (ML) approach to train and evaluate thousands of models on a clinical dataset consisting of commonly available clinical and laboratory data, along with cytokine profiles for patients (n = 150). These models were then further tested for generalizability on an out-of-sample secondary dataset (n = 120). We were able to develop a ML model for rapid and reliable screening of patients as COVID-19 positive or negative using three approaches: commonly available clinical and laboratory data, a cytokine profile, and a combination of the common data and cytokine profile. Of the tens of thousands of models automatically tested for the three approaches, all three approaches demonstrated > 92% sensitivity and > 88 specificity while our highest performing model achieved 95.6% sensitivity and 98.1% specificity. These models represent a potential effective deployable solution for COVID-19 status classification for symptomatic patients in resource-limited settings and provide proof-of-concept for rapid development of screening tools for novel emerging infectious diseases.

Demographic and clinical features of dengue fever infection in Pakistan: a cross-sectional epidemiological study.

BACKGROUND: Dengue fever caused by dengue virus is a tropical disease and is among the deadliest vector-borne diseases. The humid and hot summers of Pakistan support the probation of the vectors responsible for the transmission of viral and other parasitic diseases. METHODOLOGY: A retrospective study, from 2012- 2019, of dengue infected individuals from the Punjab province of Pakistan was carried out to analyze epidemiology, clinical and laboratory findings of subjects with dengue virus infection. Data was derived from National Institute of Health (NIH) followed by Dengue control program of Pakistan, covering the incidence rate in 36 districts of Punjab and Islamabad Capital Territory (ICT) respectively. Patients data including the presence of dengue specific antigen or/and antibodies such as NS1 and IgG/IgM were observed. The study also included the analysis of demographic data, geographic data, and the month-wise distribution of dengue cases to examine seasonal trends. RESULTS: We analyzed 25,682 dengue infected individuals. The statistical analysis revealed a significant association between genders in which male population was more affected by dengue than females. It was also noted that the middle age group was the most affected age group while the highest number of cases were reported in October. Rawalpindi and Lahore were the most affected cities in Punjab province while Islamabad represented the highest number of cases during the recent outbreak in 2019. The IgM and IgG antibodies were highly prevalent among the infected patients. CONCLUSION: Dengue is endemic in Pakistan, circulating throughout the year. Highest number of cases were observed in the month of October, September and November respectively. Association between climate change and vector-borne diseases need to be investigated in Pakistan as they significantly influence the timing and intensity of dengue and other disease outbreaks. Further exploration of hematological parameters is required to better diagnose and treat the disease. For the effective control of dengue outbreaks, awareness campaigns on sewage management and vector control along with social factors are strongly recommended for better control and eradication of the disease.

Strategical selection of maintenance type under different conditions.

Selecting the appropriate maintenance type is a challenging task that involves multiple criteria working together. This decision has a significant impact on the organization and its overall market sustainability. The primary categorization of maintenance consists of two main types: corrective maintenance and preventive maintenance. All other classifications are encompassed within these two categories. For instance, preventive maintenance can be further classified as either predictive maintenance or periodic maintenance. Given the importance of this decision, this paper discusses the optimal maintenance type under different conditions. The scale of the business, the cost of machine failure, the effect of machine failure on the production schedule, the effect of machine failure on worker safety and the workplace environment, the availability of spare parts, the lifespan of the machine, and the manufacturing process are some of the factors that are covered in this paper. This paper primarily aims to present a comprehensive literature review concerning the strategic decision-making process for selecting the appropriate maintenance type under varying conditions. Additionally, the paper incorporates various models and visual aids within its content to facilitate and guide the decision-making procedure. Corrective maintenance is usually necessary in the case of small companies, significant impact on business or production plans due to failures, potential risks to public safety, ready availability of spare parts, and when production processes are not interdependent. If these parameters are not met, preventive maintenance can be a better option. Since these circumstances frequently do not occur simultaneously, it is imperative for the business to give them significant consideration.

Appraising the lifetime private economic returns of postgraduate degrees: Evidence from Pakistan.

In Pakistan, public sector investment has significantly increased in higher education over the last two decades. This study, therefore, aims to estimate the private economic returns of Ph.D. faculty members over their non-PhD counterparts in Pakistani universities. We use questionnaire-based survey data of 784 respondents comprising Ph.D. and non-Ph.D. faculty members. In the first step, earning function is estimated for the entire group. In the next step, the lifetime private economic returns are calculated with the help of the simulation process, which yields the net present values (NPV) of the lifetime earnings for the two subgroups. Our findings show that the lifetime private economic returns of a Ph.D. degree are higher than a non-Ph.D. degree. In the case of a domestic Ph.D. degree, the average lifetime economic returns of the Ph.D. are 46.5% higher than those of non-Ph.D. faculty members. In contrast, foreign-country Ph.D. degree holders earn 29.8% extra than non-Ph.D. faculty members. Therefore, the net lifetime returns of foreign Ph.D. holders are 16.7% points less than the domestic degree holders because the cost of doing a Ph.D. degree from a foreign university is higher. Our sensitivity analysis reveals that changing the retirement age from 60 to 55 and 65 does not affect these results. However, the difference between the net returns decreases if the retirement age is 55 and increases in the case of 65. Similarly, increasing the completion time of a Ph.D. also affects the net lifetime private economic return negatively.

Do economic policy uncertainty and environment-related technologies help in limiting ecological footprint?

Economic policies related to energy and the environment are found uncertain in developing economies. Renewable energy sources are gradually increasing in energy structure (ES) with the adoption of environment-related technologies (ERT). However, least attention is paid to investigating the nexus of economic policy uncertainty (EPU), ERT, ES, and ecological footprint (EF). Therefore, this study is an effort to examine the EPU, ERT, ES, and interaction of EPU and ERT on EF for BRICS economies under the umbrella of the STIRPAT model. By using the data from 1992 to 2020, findings are estimated through "cross-sectional dependence (CD test); CIPS and CADF unit root test; Westerlund's co-integration; and CS-ARDL, AMG, and CCEMG." Findings unveiled the negative role of EPU on EF. Furthermore, the role of RE and ERT is positive and substantial in decreasing the environmental degradation in BRICS. Therefore, the BRICS economies are suggested to be consistent on economic policies to catch the positive impact of ERT. Findings are robust to the policy implications.

Immune response dynamics in COVID-19 patients to SARS-CoV-2 and other human coronaviruses.

COVID-19 serological test must have high sensitivity as well as specificity to rule out cross-reactivity with common coronaviruses (HCoVs). We have developed a quantitative multiplex test, measuring antibodies against spike (S) proteins of SARS-CoV-2, SARS-CoV, MERS-CoV, and common human coronavirus strains (229E, NL63, OC43, HKU1), and nucleocapsid (N) protein of SARS-CoV viruses. Receptor binding domain of S protein of SARS-CoV-2 (S-RBD), and N protein, demonstrated sensitivity (94% and 92.5%, respectively) in COVID-19 patients (n = 53), with 98% specificity in non-COVID-19 respiratory-disease (n = 98), and healthy-controls (n = 129). Anti S-RBD and N antibodies appeared five to ten days post-onset of symptoms, peaking at approximately four weeks. The appearance of IgG and IgM coincided while IgG subtypes, IgG1 and IgG3 appeared soon after the total IgG; IgG2 and IgG4 remained undetectable. Several inflammatory cytokines/chemokines were found to be elevated in many COVID-19 patients (e.g., Eotaxin, Gro-α, CXCL-10 (IP-10), RANTES (CCL5), IL-2Rα, MCP-1, and SCGF-b); CXCL-10 was elevated in all. In contrast to antibody titers, levels of CXCL-10 decreased with the improvement in patient health suggesting it as a candidate for disease resolution. Importantly, anti-N antibodies appear before S-RBD and differentiate between vaccinated and infected people-current vaccines (and several in the pipeline) are S protein-based.

A comprehensive review on clinical and mechanistic pathophysiological aspects of COVID-19 Malady: How far have we come?

Since its outbreak in 2019, the coronavirus disease (COVID-19) has become a pandemic, affecting more than 52 million people and causing more than 1 million mortalities globally till date. Current research reveals a wide array of disease manifestations and behaviors encompassing multiple organ systems in body and immense systemic inflammation, which have been summarized in this review. Data from a number of scientific reviews, research articles, case series, observational studies, and case reports were retrieved by utilizing online search engines such as Cochrane, PubMed, and Scopus from December 2019 to November 2020. The data for prevalence of signs and symptoms, underlying disease mechanisms and comorbidities were analyzed using SPSS version 25. This review will discuss a wide range of COVID-19 clinical presentations recorded till date, and the current understanding of both the underlying general as well as system specific pathophysiologic, and pathogenetic pathways. These include direct viral penetration into host cells through ACE2 receptors, induction of inflammosomes and immune response through viral proteins, and the initiation of system-wide inflammation and cytokine production. Moreover, peripheral organ damage and underlying comorbid diseases which can lead to short term and long term, reversible and irreversible damage to the body have also been studied. We concluded that underlying comorbidities and their pathological effects on the body contributed immensely and determine the resultant disease severity and mortality of the patients. Presently there is no drug approved for treatment of COVID-19, however multiple vaccines are now in use and research for more is underway.

Girasol, a sky imaging and global solar irradiance dataset.

The energy available in a microgrid that is powered by solar energy is tightly related to the weather conditions at the moment of generation. A very short-term forecast of solar irradiance provides the microgrid with the capability of automatically controlling the dispatch of energy. We propose a dataset to forecast Global Solar Irradiance (GSI) using a data acquisition system (DAQ) that simultaneously records sky imaging and GSI measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed the Girasol Machine (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on a solar tracker. The tracker updates its pan and tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal mount next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun's positions, has been tagged with timestamps.

Mechanistic new insights of flavonols on neurodegenerative diseases.

With a large and increasing elderly population, neurodegenerative diseases such as Parkinson's disease (PD), Huntington disease (HD), Alzheimer's disease (AD), Amyotrophic lateral sclerosis (ALS) and Multiple sclerosis (MS) have become a major and growing health problem. During the past few decades, the elderly population has grown 2.5 % every year. Unfortunately, there are no specific therapeutic remedies available to slow the onset or development of these diseases. An aging brain causes many pathophysiological changes and is the major risk factor for most of the neurodegenerative disorders. Polyphenolic compounds such as flavonols have shown therapeutic potential and can contribute to the treatment of these diseases. In this review, evidence for the beneficial neuroprotective effect of multiple flavonols is discussed and their multifactorial cellular pathways for the progressions of age-associated brain changes are identified. Moreover, the animal models of these diseases support the neuroprotective effect and target the potential of flavonols in the treatment of neurodegenerative diseases.

Using Six Sigma DMAIC Methodology and Discrete Event Simulation to Reduce Patient Discharge Time in King Hussein Cancer Center.

Short discharge time from hospitals increases both bed availability and patients' and families' satisfaction. In this study, the Six Sigma process improvement methodology was applied to reduce patients' discharge time in a cancer treatment hospital. Data on the duration of all activities, from the physician signing the discharge form to the patient leaving the treatment room, were collected through patient shadowing. These data were analyzed using detailed process maps and cause-and-effect diagrams. Fragmented and unstandardized processes and procedures and a lack of communication among the stakeholders were among the leading causes of long discharge times. Categorizing patients by their needs enabled better design of the discharge processes. Discrete event simulation was utilized as a decision support tool to test the effect of the improvements under different scenarios. Simplified and standardized processes, improved communications, and system-wide management are among the proposed improvements, which reduced patient discharge time by 54% from 216 minutes. Cultivating the necessary ownership through stakeholder analysis is an essential ingredient of sustainable improvement efforts.

Factors associated with antibiotic prescriptions for the viral origin diseases in office-based practices, 2006-2012.

OBJECTIVE: To test the potential association between time spent with a doctor and antibiotic overprescriptions in case of the common cold, runny nose, bronchitis, chest colds, flu, sore throats, and fluid in the middle ear. DESIGN: Cross-sectional study. SETTING: Office-based physicians in the US. PARTICIPANTS: A total of 261,623 patient visits recorded to office-based physicians in the US. MAIN OUTCOME MEASURES: The interest outcome was unnecessary antibiotic prescription. RESULTS: The analysis revealed five significant predictors of antibiotic prescriptions for suspected viral infections: length of doctor-patient encounter time, patient gender, spending time with a family medicine doctor, type of insurance, and the rate of antibiotic prescriptions per physician. For every additional minute a patient spent with a physician during a visit, the mean predicted probability of receiving unnecessary antibiotics decreased by 2.4%. CONCLUSIONS: This study provided evidence that physicians continue to prescribe antibiotics in avoidable cases. Policies that would monitor antibiotic prescription in office-based settings should be considered in order to control spreading of antibiotic resistance and eventually improve population health.

Statistical approach for brain cancer classification using a region growing threshold.

In brain cancer, a biopsy as an invasive procedure is needed in order to differentiate between malignant and benign brain tumor. However, in some cases, it is difficult or harmful to perform such a procedure, to the brain. The aim of this study is to investigate a new method in maximizing the probability of brain cancer type detection without actual biopsy procedure. The proposed method combines both image and statistical analysis for tumor type detection. It employed image filtration and segmentation of the target region of interest with MRI to assure an accurate statistical interpretation of the results. Statistical analysis was based on utilizing the mean, range, box plot, and testing of hypothesis techniques to reach acceptable and accurate results in differentiating between those two types. This method was performed, examined and compared on actual patients with brain tumors. The results showed that the proposed method was quite successful in distinguishing between malignant and benign brain tumor with 95% confident that the results are correct based on statistical testing of hypothesis.

Molecular interactions of human immunodeficiency virus type 1 with primary human oral keratinocytes.

Infection of the oral mucosa of human immunodeficiency virus type 1 (HIV-1)-infected individuals remains an under-evaluated and somewhat enigmatic process. Nonetheless, it is of profound importance in the ongoing AIDS pandemic, based on its potential as a site of person-to-person transmission of the virus as well as a location of HIV-1 pathogenesis and potential reservoir of disease in the setting of virally suppressive highly active antiretroviral therapy. We utilized molecular and virological techniques to analyze HIV-1 infection of primary human mucosal cells and also evaluated the proapoptotic potential of selected HIV-1 proteins in primary isolated human oral keratinocytes. Primary isolated human oral keratinocytes were plated on 0.4 microM polyethylenetetraphthalate cell culture inserts to form an in vitro oral mucosal layer. The strength of this layer in forming a barrier was determined by measuring trans-epithelial electrical current passage across the monolayer. The oral keratinocyte monolayers had trans-epithelial electrical resistance of approximately 176 to 208 omega. For viral infectivity assays, the macrophage-tropic (R5) HIV-1 strains, YU-2 and ADA, and T-cell-line-tropic (X4), NL4-3 virions, incubated with or without deoxynucleoside triphosphates (dNTPs) and/or the polyamines spermine and spermidine, were used to infect oral keratinocytes. Of importance, polyamines and dNTPs have been shown to enhance natural endogenous reverse transcription (NERT), a step essential for early lentiviral infection, and are abundantly present in human semen. The infectivities of HIV-1 strains YU-2, ADA, and NL4-3 for these primary keratinocytes were dramatically increased by the addition of physiological concentrations of dNTPs, spermine, and spermidine. Binding and viral internalization assay studies showed no differences in these oral mucosal cells, with or without NERT-altering agents. It was also observed that the recombinant, cell-free HIV-1 proteins Nef, Tat, and gp120 (R5) induced apoptosis in primary oral keratinocytes compared with the results seen with nontreated cells or cells treated with glutathione S-transferase protein as a control under similar conditions. Microarray analyses suggested that HIV-1 gp120 and Tat induce apoptosis in primary human oral keratinocytes via the Fas/FasL apoptotic pathway, whereas induction of apoptosis by Nef occurs through both Fas/FasL and mitochondrial apoptotic pathways. Thus, these findings suggest molecular mechanisms by which semen in particular, as well as other bodily fluids such as cervicovaginal secretions, could increase oral transmission of HIV-1 via increasing infectivity in confluent and low-replicating oral keratinocytes. As well, the induction of apoptosis in human oral keratinocytes with relevant HIV-1-specific proteins suggests another potential complementary mechanism by which the oral mucosa barrier may be disrupted during HIV-1 infection in vivo.