Search results for: early childhood caries

Authors: M. Lim Haslip

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This quantitative study explores the correlation between child gender and father-child interactions. The author analyzes data from videotaped interactions between African-American fathers and their boy or girl toddler to explain how African-American fathers and toddlers interact with each other and whether these interactions differ by child gender. The purpose of this study is to investigate the research question: 'How, if at all, do fathers’ speech and gestures differ when interacting with their two-year-old sons versus daughters during free play?' The objectives of this study are to describe how child gender impacts African-American fathers’ verbal communication, examine how fathers gesture and speak to their toddler by gender, and to guide interventions for low-income African-American families and their children in early language development. This study involves a sample of 41 low-income African-American fathers and their 24-month-old toddlers. The videotape data will be used to observe 10-minute father-child interactions during free play. This study uses the already transcribed and coded data provided by Dr. Meredith Rowe, who did her study on the impact of African-American fathers’ verbal input on their children’s language development. The Child Language Data Exchange System (CHILDES program), created to study conversational interactions, was used for transcription and coding of the videotape data. The findings focus on the quantity of speech, diversity of speech, complexity of speech, and the quantity of gesture to inform the vocabulary usage, number of spoken words, length of speech, and the number of object pointings observed during father-toddler interactions in a free play setting. This study will help intervention and prevention scientists understand early language development in the African-American population. It will contribute to knowledge of the role of African-American fathers’ interactions on their children’s language development. It will guide interventions for the early language development of African-American children.

Keywords: parental engagement, early language development, African-American families, quantity of speech, diversity of speech, complexity of speech and the quantity of gesture

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Authors: Gholamhossein Hosseini

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Two earliness cotton genotypes I and II, which had been developed by hybridization and backcross methods between sindise-80 as an early maturing gene parent and two other lines i.e. Red leaf and Bulgare-557 as a second parent, are subjected to different environmental conditions. The early maturing genotypes with coded names of I and II were compared with four native cotton cultivars in randomized complete block design (RCBD) with four replications in three ecological regions of Iran from 2016-2017. Two early maturing genotypes along with four native cultivars viz. Varamin, Oltan, Sahel and Arya were planted in Agricultural Research Station of Varamin, Moghan and Kashmar for evaluation. Earliness data were collected for six treatments during two years in the three regions except missing data for the second year of Kashmar. Therefore, missed data were estimated and imputed. For testing the homogeneity of error variances, each experiment at a given location or year is analyzed separately using Hartley and Bartlett’s Chi-square tests and both tests confirmed homogeneity of variance. Combined analysis of variance showed that genotypes I and II were superior in Varamin, Moghan and Kashmar regions. Earliness means and their interaction effects were compared with Duncan’s multiple range tests. Finally combined analysis of variance showed that genotypes I and II were superior in Varamin, Moghan and Kashmar regions. Earliness means and their interaction effects are compared with Duncan’s multiple range tests.

Keywords: cotton, combined, analysis, earliness

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Authors: Thea Werkhoven, Wayne Cotton

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Adult and childhood rates of obesity in Australia are health concerns of high national priority, retaining epidemic status in the populations affected. Attempts to prevent further increases in prevalence of childhood obesity in the population aged below eighteen years have had varied success. A multidisciplinary approach has been used, employing strategies in schools, through established health care system usage and public health campaigns. Over the last decade a plateau in prevalence has been reached in the youth population afflicted by obesity and interest has peaked in school based strategies to prevent and treat overweight and obesity. Of interest to this study is the importance of the tertiary training of future health educators or health professionals destined to be involved in obesity prevention and treatment strategies. Health educators and health professionals are considered instrumental to the success of prevention and treatment strategies, required to possess sufficient and accurate knowledge in order to be effective in their positions. A common influence on the success of school based health promoting activities are the weight based attitudes possessed by health educators, known to be negative and biased towards overweight or obese children during training and practice. Whilst the tertiary training of future health professionals includes minimal nutrition education, there is no mandatory training in health education or nutrition for pre-service health educators in Australian tertiary institutions. This study aimed to assess the impact of a pedagogical intervention on pre-service health educators and health professionals enrolled in a health and wellbeing elective. The intervention aimed to increase nutrition knowledge and decrease weight bias and was embedded in the twelve week elective. Participants (n=98) were tertiary students at a major Australian University who were enrolled in health (47%) and non-health related degrees (53%). A quantitative survey using four valid and reliable instruments was conducted to measured nutrition knowledge, antifat attitudes and weight stereotyping attitudes at baseline and post-intervention. Scores on each instrument were compared between time points to check if they had significantly changed and to determine the effect of the intervention on attitudes and knowledge. Antifat attitudes at baseline were considered low and decreased further over the course of the intervention. Scores representing weight bias did decrease but the change was not significant. Fat stereotyping attitudes became stronger over the course of the intervention and this change was significant. Nutrition knowledge significantly improved from baseline to post-intervention. The design of the nutrition knowledge and attitude amelioration content of the intervention was semi-successful in achieving its outcomes. While the level of nutrition knowledge was improved over the course of the intervention, an unintentional increase was observed in weight based prejudice which is known to occur in interventions that employ stigma reduction methodologies. Further research is required into a structured methodology that increases level of nutrition knowledge and ameliorates weight bias at the tertiary level. In this way training provided would help prepare future health educators with the knowledge, skills and attitudes required to be effective and bias free in their practice.

Keywords: education, intervention, nutrition, obesity

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Authors: Akankunda Veronicah Karuhanga

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Dementia is an age mental disorder that makes victims lose normal functionality that needs delicate attention. It has been technically defined as a clinical syndrome that presents a number of difficulties in speech and other cognitive functions that change someone’s behaviors and can also cause impairments in activities of daily living, not forgetting a range of neurological disorders that bring memory loss and cognitive impairment. Family members are the primary healthcare givers and therefore, the way how they handle the situation in its early stages determines future deterioration syndromes like total memory loss. Unfortunately, most family members are ignorant about this condition and in most cases, the patients are brought to our facilities when their condition was already mismanaged by family members and we thus cannot do much. For example, incontinence can be managed at early stages through potty training or toilet scheduling before resorting to 24/7 diapers which are also not good. Professional Elderly care should be understood and practiced as an extension of homes, not a dumping place for people considered “abnormal” on account of ignorance. Immediate relatives should therefore be sensitized concerning the normalcy of dementia in the context of old age so that they can be understanding and supportive of dementia patients rather than discriminating against them as present-day lepers. There is a need to skill home-based caregivers on how to handle dementia in its early stages. Unless this is done, many of our elderly homes shall be filled with patients who should have been treated and supported from their homes. This skilling of home-based caregivers is a vital intervention because until elderly care is appreciated as a human moral obligation, many transactional rehabilitation centers will crop up and this shall be one of the worst moral decadences of our times.

Keywords: dementia, family, Alzheimers, relevancy

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Authors: Nabaweesi Josephine, Namwanje Regina Germina

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Antenatal care is referred to as the totality of care given to pregnant women from conception to delivery from a certified health care setting. A number of 8 contacts is recommended throughout pregnancy, according to WHO, 2016. Antenatal services are free in Uganda courtesy of the government of Uganda, though attendance is still very low, which has continued to cause maternal and infant mortality and morbidity from preventable causes. Early booking has an advantage for proper pregnancy information sharing and pregnancy monitoring. The purpose of this study was to determine pregnant women's knowledge, attitudes, and practices towards attendance of antenatal care at Mukono General Hospital. A sample of 60 pregnant women was used, and a descriptive quantitative design was employed. Data was collected using a structured questionnaire consisting of questions about socio-demographic factors, knowledge, attitude, and practice, and this was affected using the structured interview method. Pregnant women had good practice at 90.2%, a positive attitude of 94.6%, and slightly less knowledge of 66.7%. Only 12% were knowledgeable about the number of antenatal care visits recommended, 45% had knowledge about when to initiate first antenatal care visit, and 79% had a positive attitude towards the early booking. We recommend that pregnant women are given all the necessary information regarding antenatal care with special emphasis on the recommended number of visits and when to initiate their first visit and encourage early booking in order to achieve the 8 contacts WHO policy for antenatal care since when we increase knowledge, we increase antenatal care utilization according to Anderson's behavioral model.

Keywords: ANC- antenatal care, contacts, mortality, morbidity

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Authors: Mohammad Nazri Abdul Bahari, Nurshafika Mohd Sakeh, Siti Nor Akmar Abdullah

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Basal stem rot (BSR) is the most devastating disease in oil palm. Among the oil palm pathogenic fungi, the most prevalent and virulent species associated with BSR is Ganoderma boninense. Early detection of G. boninense attack in oil palm wherein physical symptoms has not yet appeared can offer opportunities to prevent the spread of the necrotrophic fungus. However, poor understanding of molecular defense responses and roles of antifungal metabolites in oil palm against G. boninense has complicated the resolving measures. Hence, characterization of defense-related molecular responses and production of antifungal compounds during early interaction with G. boninense is of utmost important. Four month-old oil palm (Elaeis guineensis) seedlings were artificially infected with G. boninense-inoculated rubber wood block via sitting technique. RNA of samples were extracted from roots and leaves tissues at 0, 3, 7 and 11 days post inoculation (d.p.i) followed with sequencing using RNA-Seq method. Differentially-expressed genes (DEGs) of oil palm-G. boninense interaction were identified, while changes in metabolite profile will be scrutinized related to the DEGs. The RNA-Seq data generated a total of 113,829,376 and 313,293,229 paired-end clean reads from untreated (0 d.p.i) and treated (3, 7, 11 d.p.i) samples respectively, each with two biological replicates. The paired-end reads were mapped to Elaeis guineensis reference genome to screen out non-oil palm genes and subsequently generated 74,794 coding sequences. DEG analysis of phytohormone biosynthetic genes in oil palm roots revealed that at p-value ≤ 0.01, ethylene and jasmonic acid may act in antagonistic manner with salicylic acid to coordinate defense response at early interaction with G. boninense. Findings on metabolite profiling of G. boninense-infected oil palm roots and leaves are hoped to explain the defense-related compounds elicited by Elaeis guineensis in response to G. boninense colonization. The study aims to shed light on molecular defense response of oil palm at early interaction with G. boninense and promote prevention measures against Ganoderma infection.

Keywords: Ganoderma boninense, metabolites, phytohormones, RNA-Seq

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Authors: Ka C. Lam, Oluwafunmibi S. Idowu

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Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

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Authors: Hongxin Zhao, Shibing Yang, Bingming Yi, Yi Ning

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Background: A few studies have found evidence that exposure to endogenous or postmenopausal exogenous estrogens may be associated with a lower prevalence of age-related macular degeneration (AMD), but dispute over this association is ongoing due to inconsistent results reported by different studies. Objectives: To conduct a systematic review and meta-analysis to investigate the association between hormone replacement therapy (HRT) use and AMD. Methods: Relevant studies that assessed the association between HRT and AMD were searched through four databases (PubMed, Web of Science, Cochrane Library, EMBASE) and reference lists of retrieved studies. Study selection, data extraction and quality assessment were conducted by three independent reviewers. The fixed-effect meta-analyses were performed to estimate the association between HRT ever-use and AMD by pooling risk ratio (RR) or odds ratio (OR) across studies. Results: The review identified 2 prospective and 7 cross-sectional studies with 93992 female participants that reported an estimate of the association between HRT ever-use and presence of early AMD or late AMD. Meta-analyses showed that there were no statistically significant associations between HRT ever-use and early AMD (pooled RR for cohort studies was 1.04, 95% CI 0.86 - 1.24; pooled OR for cross-sectional studies was 0.91, 95% CI 0.82 - 1.01). The pooled results from cross-sectional studies also showed no statistically significant association between HRT ever-use and late AMD (OR 1.01; 95% CI 0.89 - 1.15). Conclusions: The pooled effects from observational studies published to date indicate that HRT use is associated with neither early nor late AMD. Exposure to HRT may not protect women from developing AMD.

Keywords: hormone replacement therapy, age-related macular degeneration, meta-analysis, systematic review

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Authors: Neda Valizadeh, Soudabeh Shafiee Ardestani, Arvin Najafi

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Objectives: The aim of this study is to evaluate the association of mid-regional pro-atrial natriuretic peptide (MRproANP), procalcitonin (PCT), proendothelin-1 (proET-1) levels with sepsis severity in Emergency ward patients. Materials and Methods: We assessed the predictive value of MRproANP, PCT, copeptin, and proET-1 in early sepsis among patients referring to the emergency ward with a suspected sepsis. Results-132 patients were enrolled in this study. 45 (34%) patients had a final diagnosis of sepsis. A higher percentage of patients with definite sepsis had systemic inflammatory response syndrome (SIRS) criteria at initial visit in comparison with no-sepsis patients (P<0.05) and were admitted to the hospital (P<0.05). PCT levels were higher in sepsis patients [P<0.05]. There was no significant differences for MRproANP or proET-1 in sepsis patients (P=0.47). Conclusion: A combination of SIRS criteria and PCT levels is beneficial for the early sepsis diagnosis in emergency ward patients with a suspicious infection disease.

Keywords: emergency, prolactin, sepsis, biomarkers

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Authors: Lin Ji

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The 1923 HSBC Building is one of the landmarks of Shanghai's Bund complex and is described as "one of the finest buildings from the Suez Canal to the Bering Strait". Mr George Leopold Wilson of Palmer&Turner and his design team combine the latest British design taste with Chinese elements and bring the high standard of London manufacturing to Shanghai. This paper reviews the establishment background and construction process of the Hongkong and Shanghai Bank Corporation in Shanghai, and analyzes the characteristics of various styles and ornament of HSBC. At the same time, using the research method of iconography, compared with Britain's exploration of modern design mode in the early 20th century, we can deeply understand how this "monument of world commerce and prosperity" realizes the identity construction of its financial empire in the Far East in the perfect combination of practicality and artistry.

Keywords: early 20-century Shanghai, the bund, the HSBC building, classical styles, ornament, identity construction

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Authors: Anne-Loes K. Warps, Rob A. E. M. Tollenaar, Pieter J. Tanis, Jan Willem T. Dekker

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Anastomotic leakage after colorectal cancer surgery remains a severe complication. Early diagnosis and treatment are essential to prevent further adverse outcomes. In the literature, it has been suggested that earlier reintervention is associated with better survival, but anastomotic leakage can occur with a highly variable time interval to index surgery. This study aims to evaluate the time-interval between rectal cancer resection with primary anastomosis creation and reoperation, in relation to short-term outcomes, stratified for the use of a defunctioning stoma. Methods: Data of all primary rectal cancer patients that underwent elective resection with primary anastomosis during 2013-2019 were extracted from the Dutch ColoRectal Audit. Analyses were stratified for defunctioning stoma. Anastomotic leakage was defined as a defect of the intestinal wall or abscess at the site of the colorectal anastomosis for which a reintervention was required within 30 days. Primary outcomes were new stoma construction, mortality, ICU admission, prolonged hospital stay and readmission. The association between time to reoperation and outcome was evaluated in three ways: Per 2 days, before versus on or after postoperative day 5 and during primary versus readmission. Results: In total 10,772 rectal cancer patients underwent resection with primary anastomosis. A defunctioning stoma was made in 46.6% of patients. These patients had a lower anastomotic leakage rate (8.2% vs. 11.6%, p < 0.001) and less often underwent a reoperation (45.3% vs. 88.7%, p < 0.001). Early reoperations (< 5 days) had the highest complication and mortality rate. Thereafter the distribution of adverse outcomes was more spread over the 30-day postoperative period for patients with a defunctioning stoma. Median time-interval from primary resection to reoperation for defunctioning stoma patients was 7 days (IQR 4-14) versus 5 days (IQR 3-13 days) for no-defunctioning stoma patients. The mortality rate after primary resection and reoperation were comparable (resp. for defunctioning vs. no-defunctioning stoma 1.0% vs. 0.7%, P=0.106 and 5.0% vs. 2.3%, P=0.107). Conclusion: This study demonstrated that early reinterventions after anastomotic leakage are associated with worse outcomes (i.e. mortality). Maybe the combination of a physiological dip in the cellular immune response and release of cytokines following surgery, as well as a release of endotoxins caused by the bacteremia originating from the leakage, leads to a more profound sepsis. Another explanation might be that early leaks are not contained to the pelvis, leading to a more profound sepsis requiring early reoperations. Leakage with or without defunctioning stoma resulted in a different type of reinterventions and time-interval between surgery and reoperation.

Keywords: rectal cancer surgery, defunctioning stoma, anastomotic leakage, time-interval to reoperation

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Authors: Xing Wang, Lin Feng, Xuan Zou, Hongchen liu

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Immediate Implantation after tooth extraction is considered to be the ideal way to retain the alveolar bone, but some scholars believe the aesthetic effect in the Early Implantation case are more reliable. In this study, 89 patients were added to this retrospective study up to 5 years. Assessment indicators was including the survival of the implant (peri-implant infection, implant loosening, shedding, crowns and occlusal), aesthetics (color and fullness gums, papilla height, probing depth, X-ray alveolar crest height, the patient's own aesthetic satisfaction, doctors aesthetics score), repair defects around the implant (peri-implant bone changes in height and thickness, whether the use of autologous bone graft, whether to use absorption/repair manual nonabsorbable material), treatment time, cost and the use of antibiotics.The results demonstrated that there is no significant difference in long-term success rate of immediate implantation, early implantation and delayed implantation (p> 0.05). But the results indicated immediate implantation group could get get better aesthetic results after two years (p< 0.05), but may increase the risk of complications and failures (p< 0.05). High-risk indicators include gingival recession, labial bone wall damage, thin gingival biotypes, planting position and occlusal restoration bad and so on. No matter which type of implanting methods was selected, the extraction methods and bone defect amplification techniques are observed as a significant factors on aesthetic effect (p< 0.05).

Keywords: immediate implantation, long-term effects, aesthetics area, dental implants

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Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

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During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: artificial intelligence, COVID-19, depression detection, psychiatric disorder

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Authors: Md. Ashikur Rahman

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Optimal breastfeeding practice is essential to reducing childhood morbidity and mortality and helps to achieve Millennium Development Goal (MDG). A cross-sectional study was conducted in a rural area in Dhaka district to explore the barrier to optimal breastfeeding practices. The population of this study constitutes all nursing mothers having children aged 0-6 months, and they were selected purposively. The study adopted a structured and in-depth interview procedure consisting of open and closed-ended questions. Four hundred rural nursing mothers constituted the sample of the structured interview, while 15 were involved in the in-depth interview. Among the respondent's majority (67%) were in the age group 17-25 years, with a mean age of 24.44 years. Most (39.5%) of the mothers were housewives with a secondary level of education (46.5%). About 32% of mothers started breastfeeding within one hour after birth. But delayed initiation was reported in 31.5% of mothers, whereas 36.8% of mothers forgot the exact time of initiation of breastfeeding. The main reason not to practice colostrum was mothers tried to breastfeed, but there was no milk, stated 13.8% of mothers. In addition, about one-third (34.3%) of the respondents practiced pre-lacteal feeding, and among them, 12.8% introduced sugar with water. Reasons given by the mothers for bottle-feeding was that baby was not satisfied with breast milk only; 22.0% of mothers indicated this cause. The main influence to take formula milk by their mother and mothers-in-law was stated by 18.8% of mothers. Some mothers stated that major constraints to EBF were the perception of not having enough milk (25.5 %) and babies crying seems to be hungry (8.8%). One-third of the mothers (31.5%) felt uncomfortable during breastfeeding. Access to antenatal and postnatal counseling in the study area also was a key obstacle to optimal breastfeeding practices. In a qualitative survey, some mothers believed that there was no difference between breast milk and formula milk. Colostrum feeding, pre-lacteal feeding, early initiation of breastfeeding, and exclusive breastfeeding were strongly associated with family type, family member, birth order, religion, husbands' occupation, delivery attendants and delivery type, postnatal care, and health care facilities. To reduce the barriers to the successful practice of exclusive breastfeeding, there is a need for a grass-roots approach to educating and counseling nursing mothers with identifying factors influencing or discouraging the optimal practice.

Keywords: exclusive, breast feeding, practices, Bangladesh

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Authors: Zahra Khalid, Gul Muhammad Khan, Arbab Masood Ahmad

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Cartesian Genetic Programming (CGP) is explored to design an optimal circuit capable of early stage breast cancer detection. CGP is used to evolve simple multiplexer circuits for detection of malignancy in the Fine Needle Aspiration (FNA) samples of breast. The data set used is extracted from Wisconsins Breast Cancer Database (WBCD). A range of experiments were performed, each with different set of network parameters. The best evolved network detected malignancy with an accuracy of 99.14%, which is higher than that produced with most of the contemporary non-linear techniques that are computational expensive than the proposed system. The evolved network comprises of simple multiplexers and can be implemented easily in hardware without any further complications or inaccuracy, being the digital circuit.

Keywords: breast cancer detection, cartesian genetic programming, evolvable hardware, fine needle aspiration

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Authors: Sibusiso Mkwananzi

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The health of mother and child remain at risk among pregnant adolescents. Nevertheless, these are placed in even greater jeopardy when an expectant adolescent experiences violence. This paper sought to explore the levels and determinants of expecting adolescents in five Southern African countries. The study used the most recent (2010/2015) nationally representative demographic health survey (DHS) data from Malawi, Mozambique, Namibia, Zambia, and Zimbabwe. The highest levels of violence during pregnancy occurred amongst adolescent females living in Zimbabwe at 11.4%, followed by Zambia (8.3%) and Namibia (7.7%). Lowest levels were seen in Mozambique at 3.6%. Additionally, the determinants of experiencing violence during pregnancy included educational attainment, marital status, wealth and place of residence. Expectant adolescents that had a higher likelihood of experiencing violence were married and lived predominantly in rural settings. Higher risk was also associated with lower acquisition of education and poverty. These results show a very similar pattern to the risk factors associated with early pregnancy in the region. The predictors point to issues of possible lowered empowerment amongst younger women in their relationships and the structural challenges faced by this fledgling group. Nevertheless, addressing these dynamics could go a long way in not only decreasing the likelihood of unwanted motherhood at this early stage of the life course, but indeed even ensuring the prevention of violence during wanted early pregnancy. This would lead to improved levels of maternal and child health despite younger maternal age and aid in achieving a number of sustainable development goals.

Keywords: adolescents, determinants, Southern Africa, violence during pregnancy

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Authors: Ming Zhou, Taeho Park

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Early stage technologies have been particularly challenging to manage due to high degrees of their numerous uncertainties. Most research results directly out of a research lab tend to be at their early, if not the infant stage. A long while uncertain commercialization process awaits these lab results. The majority of such lab technologies go nowhere and never get commercialized due to various reasons. Any efforts or financial resources put into managing these technologies turn fruitless. High stake naturally calls for better results, which make a patenting decision harder to make. A good and well protected patent goes a long way for commercialization of the technology. Our preliminary research showed that there was not a simple yet productive procedure for such valuation. Most of the studies now have been theoretical and overly comprehensive where practical suggestions were non-existent. Hence, we attempted to develop a simple and highly implementable procedure for efficient and scalable valuation. We thoroughly reviewed existing research, interviewed practitioners in the Silicon Valley area, and surveyed university technology offices. Instead of presenting another theoretical and exhaustive research, we aimed at developing a practical guidance that a government agency and/or university office could easily deploy and get things moving to later steps of managing early stage technologies. We provided a procedure to thriftily value and make the patenting decision. A patenting index was developed using survey data and expert opinions. We identified the most important factors to be used in the patenting decision using survey ratings. The rating then assisted us in generating good relative weights for the later scoring and weighted averaging step. More importantly, we validated our procedure by testing it with our practitioner contacts. Their inputs produced a general yet highly practical cut schedule. Such schedule of realistic practices has yet to be witnessed our current research. Although a technology office may choose to deviate from our cuts, what we offered here at least provided a simple and meaningful starting point. This procedure was welcomed by practitioners in our expert panel and university officers in our interview group. This research contributed to our current understanding and practices of managing early stage technologies by instating a heuristically simple yet theoretical solid method for the patenting decision. Our findings generated top decision factors, decision processes and decision thresholds of key parameters. This research offered a more practical perspective which further completed our extant knowledge. Our results could be impacted by our sample size and even biased a bit by our focus on the Silicon Valley area. Future research, blessed with bigger data size and more insights, may want to further train and validate our parameter values in order to obtain more consistent results and analyze our decision factors for different industries.

Keywords: technology management, early stage technology, patent, decision

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Authors: Harley Bray, Helen Pymar, Michelle Liu, Chau Pham, Tomislav Jelic, Fran Mulhall

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Background: Our study assesses the impact of the COVID-19 pandemic on early pregnancy assessment clinic (EPAC) referrals and the use of virtual consultation in Winnipeg, Manitoba. Our clinic expanded to accept referrals from all Winnipeg Emergency Department (ED)/Urgent Care (UC) sites beginning November 2019 to April 2020. By May 2020, the COVID-19 pandemic reached Manitoba and EPAC virtual care was expanded by performing hCG remotely and reviewing blood and ED/UC ultrasound results by phone. Methods: Emergency Department Information Systems (EDIS) and EPAC data reviewed ED/UC visits for pregnancy <20 weeks and vaginal bleeding 1-year pre-COVID (March 12, 2019, to March 11, 2020) and during COVID (March 12, 2020 (first case in Manitoba) to March 11, 2021). Results: There were fewer patient visits for vaginal bleeding or pregnancy of <20 weeks (4264 vs. 5180), diagnoses of threatened abortion (1895 vs. 2283), and ectopic pregnancy (78 vs. 97) during COVID compared with pre-COVID, respectively. ICD 10 codes were missing in 849 (20%) and 1183 (23%) of patients during COVID and pre-COVID, respectively. Wait times for all patient visits improved during COVID-19 compared to pre-COVID (5.1 ± 4.4 hours vs. 5.5 ± 3.8 hours), more patients received obstetrical ultrasounds, 761 (18%) vs. 787 (15%), and fewer patients returned within 30 days (1360 (32%) vs. 1848 (36%); p<0.01). EPAC saw 708 patients (218; 31% new ED/UC) during COVID-19 compared to 552 (37; 7% new ED/UC) pre-COVID. Fewer operative interventions for pregnancy loss (346 vs. 456) and retained products (236 vs. 272) were noted. Surgeries to treat ectopic pregnancy (106 vs 113) remained stable during the study time interval. Conclusion: Accurate identification of pregnancy complications was difficult, with over 20% missing ICD-10 diagnostic codes. There were fewer ED/UC visits and surgical management for threatened abortion during COVID-19, but ectopic pregnancy operative management remained unchanged.

Keywords: early pregnancy, ultrasound, COVID-19, obstetrics

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Authors: C. Silvestros, K. Mellou, T. Georgakopoulou, A. Koustenis, E. Kokkinou, C. Botsi, A. Terzidis

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'PHILOS – Emergency health response to refugee crisis' is a programme of the Greek Ministry of Health, implemented by the Hellenic Center for Disease Control and Prevention (HCDCP) funded by the Asylum, Migration and Integration Fund (AMIF) of EU’s DG Migration and Home Affairs. One of the main objectives of the program is the immunization coverage of the target – population to assure the prevention of vaccine-preventable diseases. The program foresees vaccination needs assessment of children hosted at camps at the mainland and implementation of interventions to cover the vaccination gaps in co-operation with the Ministry of Health. The National Immunization Advisory Committee in Greece recommended that MMR (Measles, Mumps, and Rubella), PCV (Pneumococcal conjugate vaccine) and HEXA (diphtheria, tetanus, pertussis, polio, hepatitis B and Haemophilus influenzae type b) vaccines should be performed in priority. Recording was completed at 24 camps (May - June 2017); 3381 children (0-18 years) were recorded. The median number of children hosted at each camp was 95 (range: 5-553). For 68% of the children, the WHO vaccination booklet was available. 44%, 48.5% and 61% of the children were vaccinated with at least one dose of PCV, HEXA, and MMR, respectively. The proportion of vaccinated children for the three vaccines mentioned above is significantly lower for the remaining doses; PCV (second dose 8%, third dose 1.3%), HEXA (second dose 13%, third dose 2.7%, forth dose 0.1%) and MMR (second dose 23%). None of the 37 (10 from Afghanistan, 3 from Bangladesh, 23 from Pakistan, 1 from Syria) recorded unaccompanied children did not have a WHO vaccination booklet and were considered unvaccinated. There is no differentiation in vaccination coverage among different ethnicities. Massive catch up vaccination was performed at 4 camps, and 671 vaccinations were performed (245 PCV, 307 HEXA, and 119 MMR). Similar interventions are planned for all camps of the country. Recording reveled gaps in vaccination coverage of the population, mainly because of the mobility of the population, the influx of refugees- which is still ongoing- and new births. Mass vaccination campaigns are considered vital in order to increase vaccination coverage, and continuous efforts are needed in order all children living at the camps to have full access to the National Childhood Immunization Program.

Keywords: vaccine preventable, refugee–migrants camps, vaccination coverage, PCV, MMR, HEXA

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Authors: Khaled Mahmud Gaddah

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Cretaceous deposits in the southeast of the Sirt Basin of Libya occur in several grabens that formed during a major phase of rifting related to the opening of the Tethys. They include continental deposits of Early Cretaceous age that belong to the Nubian Formation and marginal to fully marine deposits of Late Cretaceous age that belong to the Lidam Formation and transitional beds. The sequence was extensively sampled from twenty-two boreholes and palynologically analysed. Much of the sequence is barren. However, subordinate shales in all formations yield diverse assemblages of poorly to well preserved and thermally middle to post mature palynomorphs. Most of the assemblages contain non-marine palynomorphs (spores, pollen, and freshwater algae), although some contain rare marine elements (dinoflagellate cysts and acritarchs). The palynofloras enabled the recognition of six assemblage zones of the late Barremian-Turonian age based on the dominant and base/top occurrences of stratigraphically useful palynomorphs: AI (Afropollisspp.-Aequitriraditesspinulosus) of late Barremian age; AIIa (Scenedesmusbifidus-S. sp.) of late Barremian?-early Aptian age; AIIb (Afropollisoperculatus-A. zonatus) of Aptian age; AIII (Crybelosporitespannuceus-Afropollisjardinus) of early Albian age; AIV (Subtilisphaera sp.-Lophosphaeridiumspp.) of Cenomanian-?Turonian age; AIV (Pediastrumbifidites-Leiosphaeridiaspp.) of Cenomanian?-Turonian age. These assemblages are comparable to others from Northern Gondwana (particularly from Libya and Egypt) and correspond to established Cretaceous palynofloral provinces. Palynofacies analysis is used to interpret the depositional environments, and five palynofacies types are recognised that reflect increasing marine influence up section.

Keywords: palynology, palynomorphs, palynofacies, tethys, sirt basin

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Authors: Fathi Ihmeideh

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The study aimed at investigating parents’ attitudes towards a school-based family program in developing kindergarten children’s literacy skills. The study surveyed 160 parents of preschool children, gathering information about their attitudes toward the development of children’s early literacy. Results indicated that parents hold positive attitudes towards the school-based family literacy program. The results also revealed statistically significant differences among parents due to a number of study variables. The study concludes with putting forward a number of practical and theoretical recommendations for the field of study. Acknowledgment: This paper was made possible by NPRP grant # (NPRP 8-921-5-122) from the Qatar national research fund (a member of Qatar foundation). The statements made herein are solely the responsibility of the authors.

Keywords: parents, literacy development, preschool children, family involvement

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Authors: Abeba Nigussie Turi, Xiangming Samuel Li

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Data-driven business models are more typical for established businesses than early-stage startups that strive to penetrate a market. This paper provided an extensive discussion on the principles of data analytics for early-stage digital entrepreneurial businesses. Here, we developed data-driven decision-making (DDDM) framework that applies to startups prone to multifaceted barriers in the form of poor data access, technical and financial constraints, to state some. The startup DDDM framework proposed in this paper is novel in its form encompassing startup data analytics enablers and metrics aligning with startups' business models ranging from customer-centric product development to servitization which is the future of modern digital entrepreneurship.

Keywords: startup data analytics, data-driven decision-making, data acquisition, data generation, digital entrepreneurship

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Authors: Arvind Srivastav, Tarun Kanti Bhattacharyya

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The north-eastern, Himalayan, and Eastern Ghats Belt of India comprise of earthquake-prone, remote, and hilly terrains. Earthquakes have caused enormous damages in these regions in the past. A wireless sensor network based earthquake early warning system (EEWS) is being developed to mitigate the damages caused by earthquakes. It consists of sensor nodes, distributed over the region, that perform majority voting of the output of the seismic sensors in the vicinity, and relay a message to a base station to alert the residents when an earthquake is detected. At the heart of the EEWS is a low-power two-stage seismic sensor that continuously tracks seismic events from incoming three-axis accelerometer signal at the first-stage, and, in the presence of a seismic event, triggers the second-stage P-wave detector that detects the onset of P-wave in an earthquake event. The parameters of the P-wave detector have been optimized for minimizing detection time and maximizing the accuracy of detection.Working of the sensor scheme has been verified with seven earthquakes data retrieved from IRIS. In all test cases, the scheme detected the onset of P-wave accurately. Also, it has been established that the P-wave onset detection time reduces linearly with the sampling rate. It has been verified with test data; the detection time for data sampled at 10Hz was around 2 seconds which reduced to 0.3 second for the data sampled at 100Hz.

Keywords: earthquake early warning system, EEWS, STA/LTA, polarization, wavelet, event detector, P-wave detector

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Authors: Sam Khozama, Ali M. Mayya

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Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

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Authors: Mastewal Endeshaw Getnet

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Imunization is one of the most cost-effective and extensively adopted public health strategies for preventing child disability and mortality. Expanded Program on Immunization (EPI) was launched in 1974 with the goal of providing life-saving vaccines to all children in all and building on the success of the global smallpox eradication program. According to World Health Organization report, by 2020, all countries should have achieved 90% vaccination coverage. Many developing countries still have not achieved the goal. Ethiopia is one of Africa's developing countries. The Ethiopian Ministry of health (MoH) launched the EPI program in 1980, with the goal of achieving 90% coverage among children under the age of 1 year by 1990. Among children aged 12-23 months, complete immunization coverage was 47% based on the Ethiopian Demographic and Health Survey (EDAS) 2019 report. The coverage varies depending on the administrative region, ranging from 21% in Afar region to 89% in Amhara region, Ethiopia. Therefore, identifying risk factors for incomplete immunization among children is a key challenge, particularly in Ethiopia, which has a large geographical diversity and a predicted with 119.96 million projected population size in the year 2022. Despite its critical and challenging issue, this issue is still open and has not yet been fully investigated. Recently, a few previous studies have been conducted on the assessment of incomplete children immunization determinants. However, the majority of the studies were cross-sectional surveys that assessed only EPI coverage. Motivated by the above investigation, this study focuses on investigating determinants associated with incomplete immunization among Ethiopian children to facilitate the rate of full immunization coverage. Moreover, we consider both individual immunization and service performance-related factors to investigate incomplete children's determinants. Consequently, we adopted an ecological model in this study. Individual and environmental factors are combined in the Ecological model, which provides multilevel framework for exploring different determinants related with health behaviors. The Ethiopian Demographic and Health Survey will be used as a source of data from 2021 to achieve the objective of this study. The findings of this study will be useful to the Ethiopian government and other public health institutes to improve the coverage score of childhood immunization based on the identified risk determinants.

Keywords: incomplete immunization, children, ethiopia, ecological model

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Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi

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A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.

Keywords: eXtreme gradient boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impair, multiclass classification, ADNI, support vector machine, random forest

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Authors: Rabie Fadil, Parshuram Aarotale, Shubha Majumder, Bijay Guargain

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Hemorrhage is the loss of blood from the circulatory system and leading cause of battlefield and postpartum related deaths. Early detection of hemorrhage remains the most effective strategy to reduce mortality rate caused by traumatic injuries. In this study, we investigated the physiological changes via non-invasive cardiac signals at rest and under different hemorrhage conditions simulated through graded lower-body negative pressure (LBNP). Simultaneous electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure (BP), impedance cardiogram (ICG), and phonocardiogram (PCG) were acquired from 10 participants (age:28 ± 6 year, weight:73 ± 11 kg, height:172 ± 8 cm). The LBNP protocol consisted of applying -20, -30, -40, -50, and -60 mmHg pressure to the lower half of the body. Beat-to-beat heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean aerial pressure (MAP) were extracted from ECG and blood pressure. Systolic amplitude (SA), systolic time (ST), diastolic time (DT), and left ventricle Ejection time (LVET) were extracted from PPG during each stage. Preliminary results showed that the application of -40 mmHg i.e. moderate stage simulated hemorrhage resulted significant changes in HR (85±4 bpm vs 68 ± 5bpm, p < 0.01), ST (191 ± 10 ms vs 253 ± 31 ms, p < 0.05), LVET (350 ± 14 ms vs 479 ± 47 ms, p < 0.05) and DT (551 ± 22 ms vs 683 ± 59 ms, p < 0.05) compared to rest, while no change was observed in SA (p > 0.05) as a consequence of LBNP application. These findings demonstrated the potential of cardiac signals in detecting moderate hemorrhage. In future, we will analyze all the LBNP stages and investigate the feasibility of other physiological signals to develop a predictive machine learning model for early detection of hemorrhage.

Keywords: blood pressure, hemorrhage, lower-body negative pressure, LBNP, machine learning

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Authors: Nomfundo Ngombane, Topside E. Mathonsi

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Remote sensing and satellite imaging have been widely used to detect wildfires; nevertheless, the technologies present some limitations in terms of early wildfire detection as the technologies are greatly influenced by weather conditions and can miss small fires. The fires need to have spread a few kilometers for the technologies to provide accurate detection. The South African Advanced Fire Information System uses MODIS (Moderate Resolution Imaging Spectroradiometer) as satellite imaging. MODIS has limitations as it can exclude small fires and can fall short in validating fire vulnerability. Thus in the future, a Machine Learning algorithm will be designed and implemented for the early detection of wildfires. A simulator will be used to evaluate the effectiveness of the proposed solution, and the results of the simulation will be presented.

Keywords: moderate resolution imaging spectroradiometer, advanced fire information system, machine learning algorithm, detection of wildfires

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Authors: H. Nabahat, E. Amiri, F. AzaditalabDavoudabadi, N. Zaeri

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Tooth decay is one of the most common forms of oral and dental illness in the world, which causes huge costs of treatment, especially in high-risk groups such as people with oral dry mouth, prevention and control of it are very important. The use of traditional treatments such as extraction of drugs from medicinal plants is of paramount importance to Iran and the international community as well. The present study was conducted with the aim of investigating the antibacterial effect of the extract of Salvia officinalis and Mentha pulegium, which are the most commonly used drugs in the treatment of oral and teeth bacterial (Streptococcus mutant, Lactobacillus rhamnosis, and Actinomyces viscosis) in vitro method. In this experimental study, two herbs of Salvia and Mentha were prepared by maceration of hydroalcoholic extract, and the antibacterial effect was evaluated by broth macro dilution on streptococcal mutagen bacteria, lactobacillus rhamnosis, and viscose actinomycosis. The results were analyzed by the Whitney Mann test (P > 0.05). The results showed that the minimum inhibitory concentration (MIC) of the salmonella extract for Streptococcus mutan were 6.25 and 12.5 μg/ml, respectively, for lactobacillus of 1.56 and 3.12 μg/ml, respectively, and for actinomycosis viscose, The order of 12.5 and 100 μg/ml was obtained. As a result, broth macro dilution showed that both extracts of Salvia and Mentha had an inhibitory effect on all three species of bacteria. This effect for Salvia was significantly (P < 0.05) more than Mentha and was within the concentration range of both the extracts and had a bactericidal effect on all three bacteria.

Keywords: antibacterial effect, dental bacteria, herbal extracts , salvia officinalis, mentha pulegium

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Authors: Amarachukwu B. Isiaka, Vivian N. Anakwenze, Chinyere C. Ezemba, Chiamaka R. Ilodinso, Chikodili G. Anaukwu, Chukwuebuka M. Ezeokoli, Ugonna H. Uzoka

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Infectious diseases continue to pose significant threats to global public health, necessitating advanced and timely detection methods for effective outbreak management. This study explores the integration of artificial intelligence (AI) in the early detection and management of infectious disease outbreaks. Leveraging vast datasets from diverse sources, including electronic health records, social media, and environmental monitoring, AI-driven algorithms are employed to analyze patterns and anomalies indicative of potential outbreaks. Machine learning models, trained on historical data and continuously updated with real-time information, contribute to the identification of emerging threats. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses the role of AI in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. The synergistic collaboration between AI technologies and traditional epidemiological methods is emphasized, highlighting the potential to enhance a nation's ability to detect, respond to, and manage infectious disease outbreaks in a proactive and data-driven manner. The findings of this research underscore the transformative impact of harnessing AI for early detection and management, offering a promising avenue for strengthening the resilience of public health systems in the face of evolving infectious disease challenges. This paper advocates for the integration of artificial intelligence into the existing public health infrastructure for early detection and management of infectious disease outbreaks. The proposed AI-driven system has the potential to revolutionize the way we approach infectious disease surveillance, providing a more proactive and effective response to safeguard public health.

Keywords: artificial intelligence, early detection, disease surveillance, infectious diseases, outbreak management

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