World Health Organization Reports Research Articles

Patients’ perception of quality of anti-retroviral therapy services in south west Nigeria: a cross sectional study

Background: The World Health Organization reports that 60% of deaths from conditions requiring health care are due to poor quality of health care in low- and medium-income countries. The aim of this study is to assess quality of antiretroviral therapy (ART) services in Osun State, Nigeria.Methods: This study utilized descriptive cross sectional study design. A semi-structured interviewer-administered questionnaire was developed to collect data from 150 patients receiving ART care from two health facilities providing ART servicesResults: The mean score for perceived quality of ART care providers was 29.6 with 98.7% of respondents having high score and the mean score for perceived quality of ART Service Delivery was 56.3 with 97.3% of respondents having high score while the mean score for perceived quality of Health Facility structure was 22.1 with 79.3% of respondents having high score. Also, 96.7% of respondents strongly agreed that ART services are satisfactory; however only 22.7% of respondents strongly agreed that time spent with staff was adequate while 62.7% of respondents reported that Medical Records unit had the most delay. Age of respondents and the type of health facility were statistically significantly associated with perception of good quality of careConclusions: The time spent by patients with ART care providers needs to be optimized and the delay encountered in medical records unit should be reduced to improve quality of ART service delivery. Also, quality of ART care providers in University of Osun teaching hospital and ART facility structure at State Hospital Asubiaro should be improved.

Diabetes Prediction Tool under System on Chip Using Machine Learning Method

Extraordinary advances in biotechnology and health sciences have brought significant generation of data, such as genetic data and clinical information, generated from huge electronic health records. Data analysis is a process of studying and identifying hidden patterns from large amounts of data and drawing conclusions. In health care, this analytical process is carried out using machine learning (ML) algorithms to analyze medical data to build Machine Learning models to transform all available information into valuable knowledge. Nowadays, diabetes has become a common disease among young people, elderly people, and even children. According to the World Health Organization (WHO) report, by 2025, this number is expected to exceed 380 million. This research work performs a comparison of 5 classification techniques, namely Naive Bayes (NB), Bagging (J48), Decision Tree (J48, C4.5), K Nearest Neighbors (KNN), and Support Vector Machine (SVM), to detect diabetes at an early stage. The performances of the five algorithms are evaluated and compared on various measures like accuracy, precision, and recall. The experiments were conducted based on the diabetes database, the source from the National Institute of Diabetes, Digestive and Kidney Diseases, showing the effectiveness of using the Decision Tree (DT) technique. This effectiveness led to the choice of this method. After obtaining the DT model of the problem, the main task facing us in this work is the hardware implementation of this model. Indeed, this forecasting system can be used in other complementary works as a processing unit in a cloud, to be able to manage numerous requests. The considered solution is the hardware implementation on a Field Programmable Gate Array (FPGA) board.

A Soft Label Deep Learning to Assist Breast Cancer Target Therapy and Thyroid Cancer Diagnosis.

According to the World Health Organization Report 2022, cancer is the most common cause of death contributing to nearly one out of six deaths worldwide. Early cancer diagnosis and prognosis have become essential in reducing the mortality rate. On the other hand, cancer detection is a challenging task in cancer pathology. Trained pathologists can detect cancer, but their decisions are subjective to high intra- and inter-observer variability, which can lead to poor patient care owing to false-positive and false-negative results. In this study, we present a soft label fully convolutional network (SL-FCN) to assist in breast cancer target therapy and thyroid cancer diagnosis, using four datasets. To aid in breast cancer target therapy, the proposed method automatically segments human epidermal growth factor receptor 2 (HER2) amplification in fluorescence in situ hybridization (FISH) and dual in situ hybridization (DISH) images. To help in thyroid cancer diagnosis, the proposed method automatically segments papillary thyroid carcinoma (PTC) on Papanicolaou-stained fine needle aspiration and thin prep whole slide images (WSIs). In the evaluation of segmentation of HER2 amplification in FISH and DISH images, we compare the proposed method with thirteen deep learning approaches, including U-Net, U-Net with InceptionV5, Ensemble of U-Net with Inception-v4, Inception-Resnet-v2 encoder, and ResNet-34 encoder, SegNet, FCN, modified FCN, YOLOv5, CPN, SOLOv2, BCNet, and DeepLabv3+ with three different backbones, including MobileNet, ResNet, and Xception, on three clinical datasets, including two DISH datasets on two different magnification levels and a FISH dataset. The result on DISH breast dataset 1 shows that the proposed method achieves high accuracy of 87.77 ± 14.97%, recall of 91.20 ± 7.72%, and F1-score of 81.67 ± 17.76%, while, on DISH breast dataset 2, the proposed method achieves high accuracy of 94.64 ± 2.23%, recall of 83.78 ± 6.42%, and F1-score of 85.14 ± 6.61% and, on the FISH breast dataset, the proposed method achieves high accuracy of 93.54 ± 5.24%, recall of 83.52 ± 13.15%, and F1-score of 86.98 ± 9.85%, respectively. Furthermore, the proposed method outperforms most of the benchmark approaches by a significant margin (p <0.001). In evaluation of segmentation of PTC on Papanicolaou-stained WSIs, the proposed method is compared with three deep learning methods, including Modified FCN, U-Net, and SegNet. The experimental result demonstrates that the proposed method achieves high accuracy of 99.99 ± 0.01%, precision of 92.02 ± 16.6%, recall of 90.90 ± 14.25%, and F1-score of 89.82 ± 14.92% and significantly outperforms the baseline methods, including U-Net and FCN (p <0.001). With the high degree of accuracy, precision, and recall, the results show that the proposed method could be used in assisting breast cancer target therapy and thyroid cancer diagnosis with faster evaluation and minimizing human judgment errors.

An ontology-based study on water quality: probabilistic risk assessment of exposure to fluoride and nitrate in Shiraz drinking water, Iran using fuzzy multi-criteria group decision-making models.

World Health Organization reports that 2.2 million people die yearly from insufficient sanitary drinking water. This ontology-based study focused on investigating the chemical quality of drinking water through a new water quality index designed by fuzzy multi-criteria group decision-making methods, merged with GIS, and, secondly, surveying non-carcinogenic risk assessment of fluoride and nitrate using Monte Carlo simulation and sensitivity analysis in Shiraz's water sources. F-, NO3-, NO2-, EC, TDS, alkalinity, TH, SO42-, Cl-, and Na were applied in the WQI. The NO3- mean concentrations were 23.15 and 27.66mg/L in the cold and warm seasons, while the mean concentrations of fluoride were 0.50 and 0.46mg/L during the cold and warm period. The 95th centiles of fluoride's HQs among infants, children, teenagers, and adults were 0.56, 0.7, 0.49, and 0.4, respectively, in the cold season, which was 0.65 and 0.81, respectively, 0.57 and 0.46 for mentioned groups in the warm season. In comparison, the 95th centiles of nitrate's HQs among infants, children, teenagers, and adults were 1.27, 1.59, 1.13, and 0.9, respectively. The HQs were more than 1 for infants, children, and teenagers, so nitrate can have various adverse effects, whereas fluoride does not adversely affect all aging groups in both seasons. Also, nitrate concentration can increase the non-carcinogenic risk, which the IR and ED lead to the HQ increasing. In contrast, BW has a negative effect on risk increasing. Overall, source management of these parameters can significantly reduce the concentration of nitrate and their adverse human health effect.

Clinically Relevant β-Lactam Resistance Genes in Wastewater Treatment Plants.

Antimicrobial resistance (AMR) is one of the largest global concerns due to its influence in multiple areas, which is consistent with One Health's concept of close interconnections between people, animals, plants, and their shared environments. Antibiotic-resistant bacteria (ARB) and antibiotic-resistance genes (ARGs) circulate constantly in various niches, sediments, water sources, soil, and wastes of the animal and plant sectors, and is linked to human activities. Sewage of different origins gets to the wastewater treatment plants (WWTPs), where ARB and ARG removal efficiency is still insufficient, leading to their transmission to discharge points and further dissemination. Thus, WWTPs are believed to be reservoirs of ARGs and the source of spreading AMR. According to a World Health Organization report, the most critical pathogens for public health include Gram-negative bacteria resistant to third-generation cephalosporins and carbapenems (last-choice drugs), which represent β-lactams, the most widely used antibiotics. Therefore, this paper aimed to present the available research data for ARGs in WWTPs that confer resistance to β-lactam antibiotics, with a particular emphasis on clinically important life-threatening mechanisms of resistance, including extended-spectrum β-lactamases (ESBLs) and carbapenemases (KPC, NDM).

Current developments in nanostructurally engineered metal oxide for removal of contaminants in water

Clean water is one of the vital resources of every living being residing on this earth. However, rapid industrialization, growing human population, urbanization, global warming, extensive agricultural practices, and climatic change have deprived them of access to clean water. These factors convert clean water into wastewater by polluting water and making it poisonous due to heavy metals. 3.575 million deaths are observed from diseases caused by waste or polluted water every year, according to the World Health Organization report. Hence, wastewater treatment has become every country's primary concern. Although several conventional methods are available for wastewater treatment, their utilization has been restrained due to the high maintenance cost of equipment, the large surface area for installation, and a large amount of energy. Nanotechnology offers a lot of potential for improving the efficiency of water purification and disinfection. Nanomaterials can remove inorganic and organic pollutants, biological pollutants (fungi, bacteria, and microbes), and heavy metal ions from wastewater. Metal oxide nanoparticles (NPs) are the most diverse class of materials due to their unique properties such as good adsorption capacity, large surface area, improved catalysis, and enhanced reactivity. Hence, metal oxide NPs have applications in wastewater treatment. A comprehensive overview of the numerous metal oxide NPs in wastewater treatment has been presented in this review. Here, in detail, we have discussed zinc oxide, titanium oxide, silver oxide, tungsten oxide, iron oxide, and copper oxide NPs and their application in wastewater treatment.

Advanced simulations and screening to repurposing a 3C protease inhibitor against the rupintrivir-resistant human norovirus-induced gastroenteritis

Human norovirus (HuNoV) causes acute viral gastroenteritis in all age groups, and dehydration and severe diarrhea in the elderly. The World Health Organization reports ∼1.45 million deaths from acute gastroenteritis annually in the world. Rupintrivir, an inhibitory medicine against the human rhinovirus C3 protease, has been reported to inhibit HuNoV 3C protease. However, several HuNoV 3C protease mutations have been revealed to reduce the susceptibility of HuNoV to rupintrivir. The structural details behind rupintrivir-resistance of these single-point mutations (A105V and I109V) are not still clear. Hence, in this study, a combination of computational techniques were used to determine the rupintrivir-resistance mechanism and to propose an inhibitor against wild-type and mutant HuNoV 3C protease through structure-based virtual screening. Dynamic structural results indicated the unstable binding of rupintrivir at the cleft binding site of the wild-type and mutant 3C proteases, leading to its detachment. Our findings presented that the domain II of the HuNoV 3C protease had a critical role in binding of inhibitory molecules. Binding energy computations, steered molecular dynamics and umbrella sampling simulations confirmed that amentoflavone, the novel suggested inhibitor, strongly binds to the cleft site of all protease models and has a good structural stability in the complex system along the molecular dynamic simulations. Our in silico study proposed the selected compound as a potential inhibitor against the HuNoV 3C protease. However, additional experimental and clinical studies are required to corroborate the therapeutic efficacy of the compound.

Role of Nanotechnology in Overcoming the Multidrug Resistance in Cancer Therapy: A Review.

Cancer is one of the leading causes of morbidity and mortality around the globe and is likely to become the major cause of global death in the coming years. As per World Health Organization (WHO) report, every year there are over 10 and 9 million new cases and deaths from this disease. Chemotherapy, radiotherapy, and surgery are the three basic approaches to treating cancer. These approaches are aiming at eradicating all cancer cells with minimum off-target effects on other cell types. Most drugs have serious adverse effects due to the lack of target selectivity. On the other hand, resistance to already available drugs has emerged as a major obstacle in cancer chemotherapy, allowing cancer to proliferate irrespective of the chemotherapeutic agent. Consequently, it leads to multidrug resistance (MDR), a growing concern in the scientific community. To overcome this problem, in recent years, nanotechnology-based drug therapies have been explored and have shown great promise in overcoming resistance, with most nano-based drugs being explored at the clinical level. Through this review, we try to explain various mechanisms involved in multidrug resistance in cancer and the role nanotechnology has played in overcoming or reversing this resistance.

The World Health Organization's work and recommendations for improving the health of trans and gender diverse people.

The World Health Organization (WHO) is guided by its global programme of work and the goal that a billion more people have universal health coverage (UHC). To achieve UHC, access for those most vulnerable must be guaranteed and prioritized. WHO is committed to developing evidence-based guidance to work towards UHC for trans and gender diverse (TGD) people. This commentary describes WHO's work related to TGD people over the last decade. In 2011, WHO developed guidelines for the prevention and treatment of HIV and sexually transmitted infections (STIs) in men who have sex with men and TGD people. In 2013, the "HIV civil society reference group" called on WHO to provide specific guidance for TGD people. Values and preferences of TGD people were considered by WHO for the first time, which informed the development of the 2014 WHO Consolidated Guidelines on HIV Prevention, Diagnosis, Treatment and Care for Key Populations. The 2014 Guidelines included a comprehensive package of HIV-related health and enabling interventions with specific considerations for TGD people, as well as a specific policy brief in 2015. Regional WHO offices developed and/or supported the development of blueprints on transgender health and HIV in 2014 and 2016. A 2015 WHO report on sexual health, human rights and the law elucidated the harmful impacts of discriminatory laws on the basis of sexual orientation and gender identity. In 2019, the 11th edition of the international classification of diseases saw the removal of "transsexualism" as a mental and behavioural disorder. WHO's first guideline on self-care interventions, updated in 2021, included key considerations concerning TGD people. In 2022, WHO's updated key populations guidelines include a prioritized package of not just HIV, but also viral hepatitis and STI health interventions for TGD people. Still, a broader and more specific health approach and a greater focus on social issues are needed to better serve the health needs of TGD people. WHO's understanding and commitment to TGD people's health has evolved and improved over the past decade. Together with professional and community trans health organizations, WHO should now start developing evidence-informed global guidance on TGD health as part of its remit to support UHC to all.

An analysis of influential factors associated with rural crashes in a developing country: A case study of Iran

Road traffic deaths continue to rise, reaching 1.35 million in recent years. Road traffic injuries are the eighth leading cause of death for people of all ages. Note that there is a wide difference in the crash rate between developed and developing countries and that developed countries report much lower crash rates than developing and underdeveloped countries. World Health Organization reports that over 80% of fatal road crashes occur in developing countries, while developed countries account for about 7% of the total. The rate of road crashes in developing countries is higher than the global average, despite some measures reducing deaths over the last decade. Numerous studies have been carried out on the safety of urban roads. However, comprehensive research evaluating influential factors associated with rural crashes in developing countries is still neglected. Therefore, it is crucial to understand how factors influence the severi-ty of rural road crashes. In the present study, rural roads in Mazandaran province were considered a case study. The Crash data collected from the Iranian Legal Medicine Organization covers 2018 to 2021, including 2047 rural crash-es. Dependent variables were classified as damage crashes and injury-fatal crashes. Besides, independent variables such as driver specifications, crash specifications, environment specifications, traffic specifications, and geometrical road specifications were considered parameters. The logit model data indicate that factors associated with driver and crash specifications influence rural crashes. The type of crashes is the most critical factor influencing the severity of crashes, on which the fatal rate depends. The findings suggested that implementing solutions that minimize the effect of the factors associated with injury and death on rural roads can reduce the severity of crashes on rural roads that share the same safety issues as the case study. Further studies can also be conducted on the safety and mechanics of the vehicle by focusing the research on the types of vehicles and the sources of the damage.

KESELAMATAN BERKENDARA PADA PENGEMUDI MOBIL PENUMPANG DITINJAU DARI FAKTOR KENDARAAN DENGAN MENGGUNAKAN MODEL CONFIRMATORY FACTOR ANALYSIS (CFA)

Based on the World Health Organization (WHO) report, 1.25 million people die and more than 30 million are injured or disabled due to traffic accidents every year. WHO has also published that death due to traffic accidents is one of the non-communicable diseases with the highest number of deaths and is estimated to be the 5th leading cause of death in the world. Based on data from Dirlantas Polda Aceh for tthe last 5 years there has been an increase in the number of traffic accidents from 2013-2016. This study aims to analyze the perception of four-wheeled drivers on driving safety in Banda Aceh. The questionnaire in this study used Google Forms and was distributed to 270 respondents who drive four-wheeled vehicles in Banda Aceh. The relationship between latent variables was processed using Confirmatory Factor Analysis (CFA) and using SPSS and IBM AMOS software. The results of this data processing indicate that only the operational system (SO) and maintenance (PR) latent variables affect driving safety (KB). The operational system (SO) affects driving safety (KB) by 0.123 and the maintenance latent variable (PR) affects driving safety (KB) by 0.522.

EVALUASI PENGGUNAAN OBAT ANTIHIPERTENSI PADA PASIEN GAGAL GINJAL KRONIK (GGK) DI UNIT HEMODIALISA RSU IMELDA PEKERJA INDONESIA MEDAN

Chronic kidney failure (CKD) is a clinical symptom caused by chronic, progressive, and irreversible kidney function disorders. The World Health Organization (2017) reports that globally, CKD occurs in more than 500 million people. According to Riskesdas (2018) the prevalence of CKD based on a doctor's diagnosis in the population aged 15 years in Indonesia is 0.38% while in North Sumatra it is 0.33%. This method replaces the work normally carried out by the kidneys, namely cleansing the blood of metabolic wastes, toxic substances, and removing accumulated water in the body. This study aims to see the pattern of use of antihypertensives in patients with chronic kidney failure in hemodialysis Imelda Indonesian Workers General Hospital Medan period 2020. This study is a descriptive observation study in which data were taken retrospectively and analyzed by descriptive statistical methods. The pattern of use of antihypertensives in CKD patients in the hemodialysis unit of Imelda RSU Indonesian Workers is mostly amlodipine 13.0%, and the most widely used combination therapy is amlodipine with valsartan 14.4%.

Longitudinal Relationship between Cognitive Function and Health-Related Quality of Life among Middle-Aged and Older Patients with Diabetes in China: Digital Usage Behavior Differences.

Background: Cognitive function and health-related quality of life (HRQoL) are important issues in diabetes care. According to the China Association for Aging, it is estimated that by 2030, the number of elderly people with dementia in China will reach 22 million. The World Health Organization reports that by 2044, the number of people with diabetes in China is expected to reach 175 million. Methods: Cohort analyses were conducted based on 854 diabetic patients aged ≥45 years from the third (2015) and fourth (2018) survey of the China Health and Retirement Longitudinal Study (CHARLS). Correlation analysis, repeated-measures variance analysis, and cross-lagged panel models were used to measure the difference in digital usage behavior in the established relationship. Results: The results show that the cognitive function of middle-aged and older diabetic patients is positively correlated with HRQoL. HRQoL at T1 could significantly predict cognitive function at T2 (PCS: B = 0.12, p < 0.01; MCS: B = 0.14, p < 0.01). This relationship is more associated with individual performance than digital usage behavior. Conclusions: Unidirectional associations may exist between cognitive function and HRQoL among middle-aged and older Chinese diabetes patients. In the future, doctors and nurses can recognize the lowering of self-perceived HRQoL of middle-aged and older diabetic patients, and thus draw more attention to their cognitive function, in turn strengthening the evaluation, detection, and intervention of their cognitive function.

A subtractive proteomics and immunoinformatics approach towards designing a potential multi-epitope vaccine against pathogenic Listeriamonocytogenes

Listeria monocytogenes is the causative agent of listeriosis, which is dangerous for pregnant women, the elderly or individuals with a weakened immune system. Individuals with leukaemia, cancer, HIV/AIDS, kidney transplant and steroid therapy suffer from immunological damage are menaced. World Health Organization (WHO) reports that human listeriosis has a high mortality rate of 20–30% every year. To date, no vaccine is available to treat listeriosis. Thereby, it is high time to design novel vaccines against L. monocytogenes. Here, we present computational approaches to design an antigenic, stable and safe vaccine against the L. monocytogenes that could help to control the infections associated with the pathogen. Three vital pathogenic proteins of L. monocytogenes, such as Listeriolysin O (LLO), Phosphatidylinositol-specific phospholipase C (PI-PLC), and Actin polymerization protein (ActA), were selected using a subtractive proteomics approach to design the multi-epitope vaccine (MEV). A total of 5 Cytotoxic T-lymphocyte (CTL) and 9 Helper T-lymphocyte (HTL) epitopes were predicted from these selected proteins. To design the multi-epitope vaccine (MEV) from the selected proteins, CTL epitopes were joined with the AAY linker, and HTL epitopes were joined with the GPGPG linker. Additionally, a human β-defensin-3 (hBD-3) adjuvant was added to the N-terminal side of the final MEV construct to increase the immune response to the vaccine. The final MEV was predicted to be antigenic, non-allergen and non-toxic in nature. Physicochemical property analysis suggested that the MEV construct is stable and could be easily purified through the E. coli expression system. This in-silico study showed that MEV has a robust binding interaction with Toll-like receptor 2 (TLR2), a key player in the innate immune system. Current subtractive proteomics and immunoinformatics study provides a background for designing a suitable, safe and effective vaccine against pathogenic L. monocytogenes.

AI-based face mask detection system: a straightforward proposition to fight with Covid-19 situation.

The whole world is suffering from a novel coronavirus, which has become an epidemic. According to a World Health Organization report, this is a communicable disease, i.e., it transfers from an infected person to a healthy person. Therefore, wearing a mask is the most important precaution to protect from COVID-19. This paper presented a deep learning-based approach to design a Face Mask Detection framework to predict whether a person is wearing a mask or not. The proposed method uses a Single Shot Multibox detector as a face detector model and a deep Inception V3 architecture (SSDIV3) to extract the pertinent features of images and discriminate them in mask and without masks labels. Optimizing the SSDIV3 approach using different modeling parameters is a genuine contribution of this work. In addition to this, the system is tested and analyzed on VGG16, VGG19, Xception, Mobilenet V2 models at different modeling parameters. Furthermore, two synthesized novel Face Mask Datasets are introduced containing diversified masks (2d_printed, 3d_printed, handkerchief, transparent, natural-looking mask appearance masks) and unmask images of humans collected in outdoor and indoor environments such as parks, homes, laboratories. The experiment outcomes demonstrate that the proposed system has achieved an accuracy of 98% on the synthesized benchmark datasets, which comparatively outperforms other state-of-art methods and datasets in a real-time environment.

Effects of Motorcycle Accidents on Household Livelihood in Vihiga County, Kenya

Purpose: World Health Organization reports that more than 1.2 million people die every year due to road transport related traffic accidents. In Kenya approximately one in every five death is due to road traffic accidents. In Vihiga County, motorcycles are the leading cause of accidents with victims increasing from 140 in 2008 to 338 in 2018. Therefore, the purpose of this paper was to analyze motorcycle accident effects on household livelihood in Vihiga County, Kenya.&#x0D; Methodology: The study was modeled around the system theory. A descriptive research design was adopted with a target population of 140 respondents who were the victims of the road traffic accident were involved using the census technique. Data collection was done using questionnaires and interviews. Validity test was by construct validity while reliability test involved the use of Cronbach’ alpha. Data was analyzed into frequencies, cumulative frequencies and percentages using the Statistical Package for Social Sciences (SPSS version 16). The study findings indicated that motorcycle accidents have an effect on livelihoods because families find it difficult accessing basic needs after their bread winner is involved in a road accident. There is an association between motorcycle accidents and access to food with chi-square of 4.487 and p-value of 0.016.&#x0D; Unique contribution to practice and policy: The study recommends that the government should train all riders to impart them with skills that will help reduce accidents and also revise the driving school curriculum in order to seal loopholes that see unqualified motorcycle riders on the road. National Transport and Safety authority to make known to the public blackspot areas especially in Hamisi sub county and ensure safety measures are taken to stop motorcycle accidents. Traffic police department to reinforce traffic rules especially on the use of helmets as most of the injuries that were fatal were as a result of riding without helmets. Ministry of education through the constituency development fund committee to identify children from families with victims of motorcycle accidents and ensure their fees is paid to avoid the high school dropout cases. The government to incorporate NGOs offering humanitarian services to consider including families of victims of motorcycle accidents in their feeding program to ensure they access food. Finally, public campaign to be carried out to sensitize the motorcycle riders on importance of subscribing to NHIF so as to be able to access treatment in case of an accident.

Moving Towards Youth-Specific and Youth-Friendly Mental Health Platform a Case Study for Risk Stratification and Timely Intervention to Mitigate Mental Health Deterioration among Adolescents

Adolescents’ mental health issue has become an imperative focus of the youth development. According to WHO (World Health Organization) report, adolescents with mental health issues are more susceptible to social isolation, discrimination, stigma (which can impair their willingness to seek care), academic challenges, risk-taking behaviors, physical ill-health, and human rights abuses. Hence, we need to take efficient, customized, and collective actions to protect adolescents from further deterioration and solve their mental problems in a timely and specific approach. Out research team strives to investigate the antecedent, solution, mechanism for the youth mental health wellbeing. The projects has last two years and keep on collecting more quantitative and qualitative evidence and implications for establishing a universal-design, person-centered, and evidencebased digital therapeutics for youth development towards Post COVID-19 new normal.

Low-density lipoprotein particles in atherosclerosis.

Among the diseases causing human death, cardiovascular disease (CVD) remains number one according to the World Health Organization report in 2021. It is known that atherosclerosis is the pathological basis of CVD. Low-density lipoprotein (LDL) plays a pivotal role in the initiation and progression of atherosclerotic CVD (ASCVD). LDL cholesterol (LDL-C) is the traditional biological marker of LDL. However, large numbers of patients who have achieved the recommended LDL-C goals still have ASCVD risk. In multiple prospective studies, LDL particle (LDL-P) is reported to be more accurate in predicting CVD risk than LDL-C. LDL-Ps differ in size, density and chemical composition. Numerous clinical studies have proved that the atherogenic mechanisms of LDL-Ps are determined not only by LDL number and size but also by LDL modifications. Of note, small dense LDL (sdLDL) particles possess stronger atherogenic ability compared with large and intermediate LDL subfractions. Besides, oxidized LDL (ox-LDL) is another risk factor in atherosclerosis. Among the traditional lipid-lowering drugs, statins induce dramatic reductions in LDL-C and LDL-P to a lesser extend. Recently, proprotein convertase subtilsin/kexin type 9 inhibitors (PCSK9i) have been demonstrated to be effective in lowering the levels of LDL-C, LDL-P, as well as CVD events. In this article, we will make a short review of LDL metabolism, discuss the discordance between LDL-C and LDL-P, outline the atherogenic mechanisms of action of LDL by focusing on sdLDL and ox-LDL, summarize the methods used for measurement of LDL subclasses, and conclude the advances in LDL-lowering therapies using statins and PCSK9i.

Health professional students at the University of Illinois Chicago (HOLISTIC) Cohort Study: A protocol.

ObjectivesThe objectives of the HOLISTIC Cohort Study are to establish a prospective cohort study covering a period of three years that characterizes the health of students within and across health professional education programs at the University of Illinois Chicago (UIC) during the coronavirus disease 2019 (COVID-19) pandemic, implement an interprofessional student research team, and generate a meaningful dataset that is used to inform initiatives that improve student health. This report describes the protocol of the HOLISTIC Cohort Study, including survey development, recruitment strategy, and data management and analysis.MethodsAn interprofessional student research team has been organized with the goal of providing continuous assessment of study design and implementation across the seven health science colleges (applied health sciences, dentistry, medicine, nursing, pharmacy, public health, and social work) at the University of Illinois Chicago in Chicago, IL. To be eligible to participate in the HOLISTIC Cohort Study, students are required to be 1) age 18 years or older; 2) enrolled full- or part-time in one or more of UIC’s seven health science colleges; and 3) enrolled in a program that prepares its graduates to enter a healthcare profession. The study protocol includes a series of three recruitment waves (Spring 2021 [April 14, 2021, to May 5, 2021; completed], Spring 2022, Spring 2023). In the first recruitment wave, eligible students were sent an invitation via electronic mail (e-mail) to complete an online survey. The online survey was based on the U.S. Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System 2019 survey and the 2014 World Health Organization Report of the Strategic Advisory Group of Experts Working Group Vaccine Hesitancy Scale. Electronic informed consent and study data are collected and managed using Research Electronic Data Capture (REDCap) tools. This study utilizes convenience sampling from all seven health science colleges at UIC with a target recruitment total of 2,000 participants.Discussion and future directionsA total of 555 students across all seven health science colleges (10.8% of 5,118 students who were invited; 27.6% of target sample size) enrolled in the cohort during the first recruitment wave. The pilot data establishes the feasibility of the study during the COVID-19 pandemic. Adaptations to overcome barriers to study implementation, including the use of remote, rather than in-person, study meetings, staff training, and participant recruitment are discussed. For the second and third waves of recruitment, the student research team will seek institutional review board (IRB) approval to implement additional enrollment strategies that are tailored to each health science college, such as online newsletters, virtual townhalls, flyers on bulletin boards near classrooms tailored to each health science college.

Experimental (FT-IR, UV-Vis) spectroscopic analysis and molecular docking investigations of anti-cancer drugs Alkeran and Bicalutamide

According to the World Health Organization reports, cancer is the leading cause of death around the globe. Infectious diseases, parasitic infections, and cardiovascular diseases are in the following order. It is accounted that in 2021, 19.8 million individuals had been treated for cancer, indicating how widespread cancer has grown. Even after all the efforts in these years, research into cancer's medical implications is still an exciting area of profound investigation. In pursuit of such fundamental scientific efforts, this investigation summarizes, the assignment of specific vibrational modes of Alkeran and Bicalutamide using standard FT-IR spectral analysis and employing molecular docking techniques have identified an exact atomic level itinerary mechanism of the Alkeran and Bicalutamide and target Cellular Tumor Antigen P53 (DNA Binding) protein and Androgen Receptor binding protein, respectively. To ascertain the molecules’ bioactivity and charge transit between their frontier orbitals, the UV-Vis spectra of the molecules were examined, and the associated energy intervals were estimated by employing quantum computations of wavelength–energy relations. Molecular docking studies of Alkeran and Bicalutamide with Cellular Tumor Antigen P53 (DNA Binding) protein and Androgen Receptor binding protein, respectively, have identified binding energy and RMSD, as well as the kinds of bonds and the meticulous path taken by the drug molecule and the receptor, which can be used to improve anticancer medication action mechanisms, effectiveness, and efficacy, as well as the establishment of innovative anticancer agents.