Immense Disease Burden Research Articles

Dynamical analysis of HIV-TB co-infection transmission model in the presence of treatment for TB

The immense disease burden of tuberculosis (TB) infection is well-documented, particularly among those co-infected with HIV and TB. To better understand the transmission dynamics of HIV-TB co-infection in the absence of readily available HIV treatment, we develop a deterministic compartmental co-infection model. Our model helps to identify the effects of TB infection on the co-infection dynamics of the two diseases, especially when treatment for TB is readily available. We find that susceptibility to TB reinfection after a previous infection leads to backward bifurcation in the TB-only model when the associated reproduction number (R_0) is less than unity. However, when we make the susceptibility to TB re-infection insignificant in the model, the disease-free equilibrium of the TB-only model is locally asymptotically stable when the associated R_0 is less than unity. We conduct sensitivity and uncertainty analyses to identify the key parameters driving TB infection dynamics, using the R_0 as the response function. We discover that the transmission rate for TB, the modification parameters accounting for the infectiousness of infected individuals with TB-only, and the treatment rates for singly infected individuals with latently infected TB are the top drivers of TB infection in the given population. Our numerical simulations suggest that concentrating treatment on TB-infected individuals in the diagnosed latently infected stage (singly or dually infected with HIV) could effectively reduce the co-infection disease burden and HIV incidence in the population under study.

Minimum data set for treatment effectiveness in pyoderma gangrenosum (MIDSTEP): an international protocol of an e-Delphi study to develop a clinical physician-driven treatment effectiveness registry on behalf of the UPGRADE initiative.

Pyoderma gangrenosum (PG) is a rare inflammatory condition with an immense disease burden that remains understudied. With limited approved treatments and low-quality clinical evidence, PG continues to have poor patient outcomes. Unfortunately, improvement in PG treatments and patient care is based on additional research endeavors that can only be developed from existing high-quality data. The following protocol outlines the development of the Minimum Data Set for Treatment Effectiveness in Pyoderma gangrenosum (MIDSTEP), a core set of domains and domain items for the Pyoderma Gangrenosum Treatment Effectiveness (PyGaTE) international registry. The outcomes and benefits are focused on providing real-world data for physicians to improve their clinical decisions on PG treatment and inform clinical trial design, promoting clinical research among the international scientific community. MIDSTEP is a multi-phase project. The first phase will produce a domain item list from a literature review to take into the second phase which would finalize the core data set by an e-Delphi exercise. There will be a single stakeholder group participating together in the e-Delphi consisting of PG experts (healthcare providers, researchers, methodologists, industry representatives, and regulators), ulcerative PG patients, and PG patient advocates. The methodology outlined in the protocol is a systematic method based on several guidelines through COMET and established dermatologic registries and outcome sets with systematic methodologies of their own. The third phase will identify the instruments for the items, the 'when to measure' the items, and the platform for the registry. The last phase is the implementation and continued maintenance of the international registry PyGaTE. By solidifying a consensus on standardized outcomes and collecting information on PG treatment effectiveness in a centralized database, existing treatments can be compared more systematically and analyzed with increased evidence. MIDSTEP and the PyGaTE international registry will have the ambitious goal to generate and disseminate real-world data that can be used by all stakeholders to improve health outcomes for PG patients. Future potential for the outcome of this project includes the development of a gold-standard PG treatment.

Utilizing Social Determinants of Health Model to Understand Barriers to Medication Adherence in Patients with Ischemic Stroke: A Systematic Review.

Ischemic strokes and their recurrence create an immense disease burden globally. Therefore, preventing recurrent strokes by promoting medication adherence is crucial to reduce morbidity and mortality. In addition, understanding the barriers to medication adherence related to the social determinants of health (SDoH) could promote equity among persons with ischemic stroke. To explore the barriers to medication adherence among patients with ischemic stroke through the SDoH. This systematic review included studies published between January 2018 and December 2022 identified through PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text. The descriptions of the studies were systematically summarized and discussed based on the SDoH from the US Healthy People 2030 initiative. Eight studies met the inclusion criteria and were included in this review. The most common barrier to adherence was inappropriate medication beliefs, medication side effects, and patient-physician relationship, which relate to the dimensions of healthcare access and quality. Health literacy and health perception, dependent on education access and quality, frequently influenced adherence. Other social determinants, such as financial strain and social and community context, were found to alter adherence behaviors. No study addressed the neighborhood and built environment domain. We found that cognitive impairment is another factor that impacts adherence outcomes among stroke patients. Multifaceted approaches are needed to address the SDoH to improve medication adherence among patients with ischemic stroke. This review emphasized strategies, including patient education, provider-patient communication, social support, health literacy, technology, and policy advocacy to enhance adherence.

COVID-19 of differing severity: from bulk to single-cell expression data analysis

ABSTRACT Coronavirus disease 2019 (COVID-19) is raging worldwide and causes an immense disease burden. Despite this, the biomarkers and targeting drugs of COVID-19 of differing severity remain largely unknown. Based on the GSE164805 dataset, we identified modules most critical for mild COVID-19 (mCOVID-19) and severe COVID-19 (sCOVID-19) through WGCNA, respectively. We subsequently constructed a protein–protein interaction network, and detected 16 hub genes for mCOVID-19 and 10 hub genes for sCOVID-19, followed by the prediction of upstream transcription factors (TFs) and kinases. The enrichment analysis then showed downregulation of TNFA signaling via NFKB for mCOVID-19, as well as downregulation of MYC targets V1 for sCOVID-19. Infiltration degrees of many immune cells, such as macrophages, were also sharply different between mCOVID-19 and sCOVID-19 samples. Predicted protein targeting drugs with the highest scores nearly all belong to naturally derived or synthetic glucocorticoids. For the two single-cell RNA-seq datasets, we explored the expression distribution of hub genes for mCOVID-19/sCOVID-19 in each cell type. The expression levels of PRKCA, MCM5, TYMS, RBBP4, BCL6, FLOT1, KDM6B, and TLR2 were found to be cell-type-specific. Furthermore, the expression levels of 10 hub genes for mCOVID-19 were significantly upregulated in PBMCs between eight healthy controls and eight mCOVID-19 patients at our institution. Collectively, we detected critical modules, pathways, TFs, kinases, immune cells, targeting drugs, hub genes, and their expression distributions in different cell types that may involve the pathogenesis of COVID-19 of differing severity, which may propel earlier diagnosis and more effective treatment of this intractable disease in the future.

Understanding the Spectrum of Esophageal Cancer Disparities

Esophageal cancer carries an immense burden of disease. Despite advances in cancer care, the survival rate of esophageal cancer has not significantly improved over the last 30 years. The burden of disease is greater on minorities, immigrants, and lower socioeconomic groups in the United States. Research has consistently shown that these communities suffer from persistently worse outcomes. Minorities are noted to have increased risk of developing esophageal cancer, have worse stage at presentation, and limited access to the newest advancements and best treatment options. The reason for these disparities is multifactorial and the US health system must address and correct the underlying reasons to ensure just health care for all.

Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis.

Type 2 diabetes (T2D) has an immense disease burden, affecting millions of people worldwide and costing billions of dollars in treatment. As T2D is a multifactorial disease with both genetic and nongenetic influences, accurate risk assessments for patients are difficult to perform. Machine learning has served as a useful tool in T2D risk prediction, as it can analyze and detect patterns in large and complex data sets like that of RNA sequencing. However, before machine learning can be implemented, feature selection is a necessary step to reduce the dimensionality in high-dimensional data and optimize modeling results. Different combinations of feature selection methods and machine learning models have been used in studies reporting disease predictions and classifications with high accuracy. The purpose of this study was to assess the use of feature selection and classification approaches that integrate different data types to predict weight loss for the prevention of T2D. The data of 56 participants (ie, demographic and clinical factors, dietary scores, step counts, and transcriptomics) were obtained from a previously completed randomized clinical trial adaptation of the Diabetes Prevention Program study. Feature selection methods were used to select for subsets of transcripts to be used in the selected classification approaches: support vector machine, logistic regression, decision trees, random forest, and extremely randomized decision trees (extra-trees). Data types were included in different classification approaches in an additive manner to assess model performance for the prediction of weight loss. Average waist and hip circumference were found to be different between those who exhibited weight loss and those who did not exhibit weight loss (P=.02 and P=.04, respectively). The incorporation of dietary and step count data did not improve modeling performance compared to classifiers that included only demographic and clinical data. Optimal subsets of transcripts identified through feature selection yielded higher prediction accuracy than when all available transcripts were included. After comparison of different feature selection methods and classifiers, DESeq2 as a feature selection method and an extra-trees classifier with and without ensemble learning provided the most optimal results, as defined by differences in training and testing accuracy, cross-validated area under the curve, and other factors. We identified 5 genes in two or more of the feature selection subsets (ie, CDP-diacylglycerol-inositol 3-phosphatidyltransferase [CDIPT], mannose receptor C type 2 [MRC2], PAT1 homolog 2 [PATL2], regulatory factor X-associated ankyrin containing protein [RFXANK], and small ubiquitin like modifier 3 [SUMO3]). Our results suggest that the inclusion of transcriptomic data in classification approaches for prediction has the potential to improve weight loss prediction models. Identification of which individuals are likely to respond to interventions for weight loss may help to prevent incident T2D. Out of the 5 genes identified as optimal predictors, 3 (ie, CDIPT, MRC2, and SUMO3) have been previously shown to be associated with T2D or obesity. ClinicalTrials.gov NCT02278939; https://clinicaltrials.gov/ct2/show/NCT02278939.

314.1: Response to a Pandemic: The Fall and Rise of Kidney Transplantation in the United States

Background: Following the outbreak of COVID-19 in the United States, the number of kidney waitlist additions and living-donor and deceased-donor kidney transplants (LDKT/DDKT) decreased substantially but began recovering within a few months. Since then, there have been several additional waves of infection, most notably, the Delta and current Omicron surge beginning in August and December 2021, respectively. By 1/12/2022, the Omicron surge peaked at 802,699 confirmed new cases, compared to a pre-Omicron peak of 251,772 confirmed new cases on 1/8/2021, demonstrating an immense disease burden on the American population that could have been enough to disrupt transplant practices. Methods: Using data from the Scientific Registry of Transplant Recipients (SRTR), we compared observed waitlist registrations, waitlist mortality, waitlist removal, LDKT, and DDKT in the United States over four distinct pandemic waves to expected events based on calculations from pre-epidemic and non-wave pandemic data, while accounting for seasonality and secular trends. Four distinct time periods/waves of high COVID-19 incidence in the United States were identified for analysis. Wave 1 was defined March 15, 2020 – May 31, 2020; Wave 2 was defined December 1, 2020 – January 31, 2021; Wave 3 was defined August 1, 2021 – September 30, 2021; and Wave 4 was defined December 1, 2021 – January 31, 2022. Waves 1, 2, 3, and 4 were referred to as the Initial, Winter 2021, Delta, and Omicron waves during our analysis. Results: Although the number of daily waitlist additions has been increasing since May 2020, the size of the active waitlist has consistently declined, reaching a minimum of 53,180 on 1/31/2022. The recent Omicron surge knocked LDKT from 12% below expected (IRR = 0.820.880.95) during the Delta wave to 34% below expected (IRR = 0.610.660.71). DDKT, in contrast, was relatively unaffected by the Omicron wave (IRR = 0.830.870.91 and 0.820.850.89 during the Delta and Omicron waves, respectively). Waitlist death declined from 20% above expected (IRR = 1.111.201.30) during Delta to 19% below expected (IRR = 0.740.810.89) during Omicron, whereas waitlist removal remained at expected levels throughout both (IRR = 0.951.041.13 and 0.991.081.18 during Delta and Omicron, respectively). Note, this was the first time that an outcome returned to expected levels since the start of the pandemic. Conclusions: The Omicron wave brought substantial declines in DDKT and LDKT that have not been seen since the Initial wave. Due to vaccine mandates in place at many transplant centers across the nation, the American SRTR population now has high levels of vaccination including high levels of second and third booster shots. Hence, despite the Omicron wave’s exceptionally high COVID-19 infection incidence in the American national cohort, there was no increase in waitlist mortality, likely due to vaccination.Ben-Dov family. K24AI144954 from National Institute of Allergy and Infectious Diseases (NIAID). K01DK101677 from National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).

Effectiveness of potential antiviral treatments in COVID-19 transmission control: a modelling study

BackgroundNovel coronavirus disease 2019 (COVID-19) causes an immense disease burden. Although public health countermeasures effectively controlled the epidemic in China, non-pharmaceutical interventions can neither be maintained indefinitely nor conveniently implemented globally. Vaccination is mainly used to prevent COVID-19, and most current antiviral treatment evaluations focus on clinical efficacy. Therefore, we conducted population-based simulations to assess antiviral treatment effectiveness among different age groups based on its clinical efficacy.MethodsWe collected COVID-19 data of Wuhan City from published literature and established a database (from 2 December 2019 to 16 March 2020). We developed an age-specific model to evaluate the effectiveness of antiviral treatment in patients with COVID-19. Efficacy was divided into three types: (1) viral activity reduction, reflected as transmission rate decrease [reduction was set as v (0–0.8) to simulate hypothetical antiviral treatments]; (2) reduction in the duration time from symptom onset to patient recovery/removal, reflected as a 1/γ decrease (reduction was set as 1–3 days to simulate hypothetical or real-life antiviral treatments, and the time of asymptomatic was reduced by the same proportion); (3) fatality rate reduction in severely ill patients (fc) [reduction (z) was set as 0.3 to simulate real-life antiviral treatments]. The population was divided into four age groups (groups 1, 2, 3 and 4), which included those aged ≤ 14; 15–44; 45–64; and ≥ 65 years, respectively. Evaluation indices were based on outbreak duration, cumulative number of cases, total attack rate (TAR), peak date, number of peak cases, and case fatality rate (f).ResultsComparing the simulation results of combination and single medication therapy s, all four age groups showed better results with combination medication. When 1/γ = 2 and v = 0.4, age group 2 had the highest TAR reduction rate (98.48%, 56.01–0.85%). When 1/γ = 2, z = 0.3, and v = 0.1, age group 1 had the highest reduction rate of f (83.08%, 0.71–0.12%).ConclusionsAntiviral treatments are more effective in COVID-19 transmission control than in mortality reduction. Overall, antiviral treatments were more effective in younger age groups, while older age groups showed higher COVID-19 prevalence and mortality. Therefore, physicians should pay more attention to prevention of viral spread and patients deaths when providing antiviral treatments to patients of older age groups.

The Untapped Social Impact of Artificial Intelligence for Breast Cancer Screening in Developing Countries: A Critical Commentary of DeepMind

The Untapped Social Impact of Artificial Intelligence for Breast Cancer Screening in Developing Countries: A Critical Commentary of DeepMind

Effectiveness of Pharmacological Interventions to Control COVID-19: An Age-Specific Modelling Study

Background: Novel coronavirus disease 2019 (COVID-19) causes an immense disease burden. Only drugs or vaccines can eliminate the virus. Methods: We adopted our age-specific transmission model by susceptible-exposed-infectious -critically ill-asymptomatic-removed (SEICAR) model. Effects of different drug types were simulated by changing transmission rate (β), critical case fatality rate (fc), and disease duration of each age group. Evaluation indexes were based on outbreak duration(OD), cumulative number of cases(CNC), total attack rate(TAR), peak date(PD), number of peak cases(NPC), and case fatality rate(f). Findings: When without intervention, changing in β and disease duration, as the age increased, OD decreased, TAR increased, PD advanced, CCN and NPC initially increased and then decreased, while f decreased first and then increased. When disease duration and β remained unchanged, changing fc did not affect the epidemic. All age groups had 40% shorter disease duration but unchanged fc, while β was reduced by 60%, which reduced TAR of group 1 (≤14 years) from 2·35% to 0·09%; f of group 4 (≥65 years) was reduced from 1·04% to 0·05%. Interpretation: Drugs had different age-dependent effects. If a drug can control the disease duration or β of all age groups, younger people would have the fastest transmission control and seniors will have the best improvement in disease severity. Funding: The Bill & Melinda Gates Foundation (INV-005834); the Science and Technology Program of Fujian Province (No: 2020Y0002), and the Xiamen New Coronavirus Prevention and Control Emergency Tackling Special Topic Program (No: 3502Z2020YJ03).Declaration of Interests: The authors declare no competing interests.

Guns, scalpels, and sutures: The cost of gunshot wounds in children and adolescents.

Firearm injuries are the second leading cause of death among US children. While injury prevention has been shown to be effective for blunt mechanisms of injury, the rising incidence of accidental gunshot wounds, school shootings, and interpersonal gun violence suggests otherwise for firearm-related injuries. The purpose of the study is to describe the incidence, injury severity, and institutional costs of pediatric gun-related injuries in Colorado. Pediatric patients (≤18 years), who sustained firearm injuries between 2008 and 2018, were identified from the trauma registries of three pediatric trauma centers in Colorado. Patients were stratified based on age: those younger than 14 years were defined as children and those 15 years to 18 years as adolescents. Our cohort (n = 308) was predominantly male (87%), with a median age of 14 years. The overall mortality rate was 11% (34/308), with significantly fewer children (5%) dying from their injuries when compared with adolescents (14%; p = 0.04). Sixty-five (21%) patients required blood product transfusions, with 23 (7.4%) patients receiving a massive transfusion. Overall, 52% (161/308) required a major operation, with 15% undergoing an exploratory laparotomy. One third (4/13) of the patients who had a thoracotomy in the emergency department survived to hospital discharge. Overall, 14.0% of patients had psychiatric follow-up at both 30 days and 1 year. The readmission rate for complications was 11.6% at 30 days and 14% at 1 year. The total cost of care for all pediatric firearm-related injuries was approximately US $26 million. The survivors of pediatric firearm injuries experience high operative and readmission rates, sustain long-term morbidities, and suffer from mental health sequelae. Combining these factors with the economic impact of these injuries highlights the immense burden of disease. This burden may be palliated by a multipronged approach, which includes the development and dissemination of injury prevention strategies and better follow-up care for these patients. Epidemiological, Level III.

Convergent Antibody Signatures in Human Dengue

Dengue is the most prevalent mosquito-borne viral disease in humans, and the lack of early prognostics, vaccines, and therapeutics contributes to immense disease burden. To identify patterns that could be used for sequence-based monitoring of the antibody response to dengue, we examined antibody heavy-chain gene rearrangements in longitudinal peripheral blood samples from 60 dengue patients. Comparing signatures between acute dengue, postrecovery, and healthy samples, we found increased expansion of B cell clones in acute dengue patients, with higher overall clonality in secondary infection. Additionally, we observed consistent antibody sequence features in acute dengue in the highly variable major antigen-binding determinant, complementarity-determining region 3 (CDR3), with specific CDR3 sequences highly enriched in acute samples compared to postrecovery, healthy, or non-dengue samples. Dengue thus provides a striking example of a human viral infection where convergent immune signatures can be identified in multiple individuals. Such signatures could facilitate surveillance of immunological memory in communities.

Noroviruses: The Perfect Human Pathogens?

Noroviruses: The Perfect Human Pathogens?

Natural Products as Leads for Tuberculosis Drug Development

New therapeutics are urgently needed to combat the immense disease burden of tuberculosis and related mycobacterial diseases worldwide. Natural products continue to provide leads for the development of novel drugs to treat the rapidly growing numbers of patients with multi- and extensively-drug resistant tuberculosis. This review presents natural products and synthesized analogues with anti-mycobacterial activity published between 2006 through 2009. Structure activity relationships, synthetic analogues and newly reported activities of known compounds reported during this period are also included.

Provision of cancer care in Eastern Europe

During a visit to Poland and Croatia in February, I heard from oncologists about the effect that the political developments in this region have had on cancer incidence, detection, and treatment. Poland and Croatia ended the 1980s in a similar position. Both countries had relatively liberal communist governments and were open to Western influences. Their experiences since then have been dramatically different. Poland now stands on the brink of EU membership, possibly as early as 2004, whereas Croatia, after suffering a damaging war of independence in the early 1990s, is barely on the starting blocks. Cancer treatment was given high priority in east-central Europe throughout the twentieth century, even during periods of financial crisis and rapid change. Services set up during the relatively prosperous 1970s were maintained during the financial crisis of the eighties and subsequent upheavals. Marie Curie herself opened the Radium Institute in Warsaw in 1932 (figure 1), with a gift of a gram of radium from the USA. The original institute was burned down during the Second World War but rebuilt a few years later, and still functions as the Maria Sklodowska-Curie Memorial Cancer Centre and Institute of Oncology (figure 2). This institute now serves all of Greater Warsaw, an area covering a population of about five million people. About a fifth of new cancer cases in Poland are referred there, and it offers a full range of modern treatment modalities including combination therapies. “Over 60% of radiotherapy and 70% of chemotherapy is now delivered on an outpatient basis, or in a day hospital, which is less expensive and which the patients prefer,” says its director, Marek Nowacki. In addition, because the Polish currency, the zloty, is now fully convertible to other currencies, it is possible to import expensive equipment and drugs from the West. Oncologists in the institute are currently involved in close to 200 different clinical trials, involving drugs, radiotherapy, mixed modalities, and novel biological treatment methods. Many are conducted in According to the World Health Organization, in 1990, a 15-year-old boy from the former Soviet bloc was less likely to reach his sixtieth birthday than his counterparts in many less developed countries, including China and India. But disease patterns in Eastern Europe were already similar to those in the established market economies. This immense burden of disease was almost entirely due to an “epidemic” of cancer and other noncommunicable diseases. Since then, some of these transitional countries have made substantial progress in cancer control. However, it is still predicted that at least one in every five citizens of Eastern Europe will die from cancer. Reportage