Modeling of malaria vaccine effectiveness on disease burden and drug resistance in 42 African countries.

BACKGROUND: The emergence of antimalarial drug resistance poses a major threat to effective malaria treatment and control. This study aims to inform policymakers and vaccine developers on the potential of an effective malaria vaccine in reducing drug-resistant infections. METHODS: A compartmental model estimating cases, drug-resistant cases, and deaths averted from 2021 to 2030 with a vaccine against Plasmodium falciparum infection administered yearly to 1-year-olds in 42 African countries. Three vaccine efficacy (VE) scenarios and one scenario of rapidly increasing drug resistance are modeled. RESULTS: When VE is constant at 40% for 4 years and then drops to 0%, 235.7 (Uncertainty Interval [UI] 187.8-305.9) cases per 1000 children, 0.6 (UI 0.4-1.0) resistant cases per 1000, and 0.6 (UI 0.5-0.9) deaths per 1000 are averted. When VE begins at 80% and drops 20 percentage points each year, 313.9 (UI 249.8-406.6) cases per 1000, 0.9 (UI 0.6-1.3) resistant cases per 1000, and 0.9 (UI 0.6-1.2) deaths per 1000 are averted. When VE remains 40% for 10 years, 384.7 (UI 311.7-496.5) cases per 1000, 1.0 (0.7-1.6) resistant cases per 1000, and 1.1 (UI 0.8-1.5) deaths per 1000 are averted. Assuming an effective vaccine and an increase in current levels of drug resistance to 80% by 2030, 10.4 (UI 7.3-15.8) resistant cases per 1000 children are averted. CONCLUSIONS: Widespread deployment of a malaria vaccine could substantially reduce health burden in Africa. Maintaining VE longer may be more impactful than a higher initial VE that falls rapidly.

Malaria can become resistant to the drugs used to treat it, posing a major threat to malaria treatment and control. An effective vaccine has the potential to reduce both resistant infections and antimalarial drug use. However, how successfully a vaccine can protect against infection (vaccine efficacy) and the impact of increasing drug resistance remain unclear. Using a mathematical model, we estimate the impact of malaria vaccination in 42 African countries over a 10-year period in multiple scenarios with differing vaccine efficacy and drug resistance. Our model suggests that a moderately effective vaccine with sustained protection over a long period could avert more resistant infections and deaths than a vaccine that is highly protective initially but lowers in efficacy over time. Nevertheless, implementation of an effective malaria vaccine should be accelerated to mitigate the health and economic burden of drug resistance.

Deconstructing the effects of stochasticity on transmission of hospital-acquired infections in ICUs.

The inherent stochasticity in transmission of hospital-acquired infections (HAIs) has complicated our understanding of transmission pathways. It is particularly difficult to detect the impact of changes in the environment on acquisition rate due to stochasticity. In this study, we investigated the impact of uncertainty (epistemic and aleatory) on nosocomial transmission of HAIs by evaluating the effects of stochasticity on the detectability of seasonality of admission prevalence. For doing so, we developed an agent-based model of an ICU and simulated the acquisition of HAIs considering the uncertainties in the behaviour of the healthcare workers (HCWs) and transmission of pathogens between patients, HCWs, and the environment. Our results show that stochasticity in HAI transmission weakens our ability to detect the effects of a change, such as seasonality patterns, on acquisition rate, particularly when transmission is a low-probability event. In addition, our findings demonstrate that data compilation can address this issue, while the amount of required data depends on the size of the said change and the degree of uncertainty. Our methodology can be used as a framework to assess the impact of interventions and provide decision-makers with insight about the minimum required size and target of interventions in a healthcare facility.

Work of Breathing During Proportional Assist Ventilation as a Predictor of Extubation Failure.

BACKGROUND: Despite decades of research on predictors of extubation success, use of ventilatory support after extubation is common and 10-20% of patients require re-intubation. Proportional assist ventilation (PAV) mode automatically calculates estimated total work of breathing (total WOB). Here, we assessed the performance of total WOB to predict extubation failure in invasively ventilated subjects. METHODS: This prospective observational study was conducted in 6 adult ICUs at an academic medical center. We enrolled intubated subjects who successfully completed a spontaneous breathing trial, had a rapid shallow breathing index < 105 breaths/min/L, and were deemed ready for extubation by the primary team. Total WOB values were recorded at the end of a 30-min PAV trial. Extubation failure was defined as any respiratory support and/or re-intubation within 72 h of extubation. We compared total WOB scores between groups and performance of total WOB for predicting extubation failure with receiver operating characteristic curves. RESULTS: Of 61 subjects enrolled, 9.8% (n = 6) required re-intubation, and 50.8% (n = 31) required any respiratory support within 72 h of extubation. Median total WOB at 30 min on PAV was 0.9 J/L (interquartile range 0.7-1.3 J/L). Total WOB was significantly different between subjects who failed or were successfully extubated (median 1.1 J/L vs 0.7 J/L, P = .004). The area under the curve was 0.71 [95% CI 0.58-0.85] for predicting any requirement of respiratory support and 0.85 [95% CI 0.69-1.00] for predicting re-intubation alone within 72 h of extubation. Total WOB cutoff values maximizing sensitivity and specificity equally were 1.0 J/L for any respiratory support (positive predictive value [PPV] 70.0%, negative predictive value [NPV] 67.7%) and 1.3 J/L for re-intubation (PPV 26.3%, NPV 97.6%). CONCLUSIONS: The discriminative performance of a PAV-derived total WOB value to predict extubation failure was good, indicating total WOB may represent an adjunctive tool for assessing extubation readiness. However, these results should be interpreted as preliminary, with specific thresholds of PAV-derived total WOB requiring further investigation in a large multi-center study.

Multiple models for outbreak decision support in the face of uncertainty.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Investigating the effects of absolute humidity and movement on COVID-19 seasonality in the United States.

Mounting evidence suggests the primary mode of SARS-CoV-2 transmission is aerosolized transmission from close contact with infected individuals. While transmission is a direct result of human encounters, falling humidity may enhance aerosolized transmission risks similar to other respiratory viruses (e.g., influenza). Using Google COVID-19 Community Mobility Reports, we assessed the relative effects of absolute humidity and changes in individual movement patterns on daily cases while accounting for regional differences in climatological regimes. Our results indicate that increasing humidity was associated with declining cases in the spring and summer of 2020, while decreasing humidity and increase in residential mobility during winter months likely caused increases in COVID-19 cases. The effects of humidity were generally greater in regions with lower humidity levels. Given the possibility that COVID-19 will be endemic, understanding the behavioral and environmental drivers of COVID-19 seasonality in the United States will be paramount as policymakers, healthcare systems, and researchers forecast and plan accordingly.

Biological effects of inhaled crude oil vapor V. Altered biogenic amine neurotransmitters and neural protein expression.

Workers in the oil and gas industry are at risk for exposure to a number of physical and chemical hazards at the workplace. Chemical hazard risks include inhalation of crude oil or its volatile components. While several studies have investigated the neurotoxic effects of volatile hydrocarbons, in general, there is a paucity of studies assessing the neurotoxicity of crude oil vapor (COV). Consequent to the 2010 Deepwater Horizon (DWH) oil spill, there is growing concern about the short- and long-term health effects of exposure to COV. NIOSH surveys suggested that the DWH oil spill cleanup workers experienced neurological symptoms, including depression and mood disorders, but the health effects apart from oil dispersants were difficult to discern. To investigate the potential neurological risks of COV, male Sprague-Dawley rats were exposed by whole-body inhalation to COV (300 ppm; Macondo surrogate crude oil) following an acute (6 h/d × 1 d) or sub-chronic (6 h/d × 4 d/wk. × 4 wks) exposure regimen. At 1, 28 or 90 d post-exposure, norepinephrine (NE), epinephrine (EPI), dopamine (DA) and serotonin (5-HT) were evaluated as neurotransmitter imbalances are associated with psychosocial-, motor- and cognitive- disorders. Sub-chronic COV exposure caused significant reductions in NE, EPI and DA in the dopaminergic brain regions, striatum (STR) and midbrain (MB), and a large increase in 5-HT in the STR. Further, sub-chronic exposure to COV caused upregulation of synaptic and Parkinson's disease-related proteins in the STR and MB. Whether such effects will lead to neurodegenerative outcomes remain to be investigated.

Cost-effectiveness of carbapenem-resistant Enterobacteriaceae (CRE) surveillance in Maryland.

OBJECTIVE: We analyzed the efficacy, cost, and cost-effectiveness of predictive decision-support systems based on surveillance interventions to reduce the spread of carbapenem-resistant Enterobacteriaceae (CRE). DESIGN: We developed a computational model that included patient movement between acute-care hospitals (ACHs), long-term care facilities (LTCFs), and communities to simulate the transmission and epidemiology of CRE. A comparative cost-effectiveness analysis was conducted on several surveillance strategies to detect asymptomatic CRE colonization, which included screening in ICUs at select or all hospitals, a statewide registry, or a combination of hospital screening and a statewide registry. SETTING: We investigated 51 ACHs, 222 LTCFs, and skilled nursing facilities, and 464 ZIP codes in the state of Maryland. PATIENTS OR PARTICIPANTS: The model was informed using 2013-2016 patient-mix data from the Maryland Health Services Cost Review Commission. This model included all patients that were admitted to an ACH. RESULTS: On average, the implementation of a statewide CRE registry reduced annual CRE infections by 6.3% (18.8 cases). Policies of screening in select or all ICUs without a statewide registry had no significant impact on the incidence of CRE infections. Predictive algorithms, which identified any high-risk patient, reduced colonization incidence by an average of 1.2% (3.7 cases) without a registry and 7.0% (20.9 cases) with a registry. Implementation of the registry was estimated to save $572,000 statewide in averted infections per year. CONCLUSIONS: Although hospital-level surveillance provided minimal reductions in CRE infections, regional coordination with a statewide registry of CRE patients reduced infections and was cost-effective.

Analysis of the potential impact of durability, timing, and transmission blocking of COVID-19 vaccine on morbidity and mortality.

BACKGROUND: COVID-19 vaccines have been approved and made available. While questions of vaccine allocation strategies have received significant attention, important questions remain regarding the potential impact of the vaccine given uncertainties regarding efficacy against transmission, availability, timing, and durability. METHODS: We adapted a susceptible-exposed-infectious-recovered (SEIR) model to examine the potential impact on hospitalization and mortality assuming increasing rates of vaccine efficacy, coverage, and administration. We also evaluated the uncertainty of the vaccine to prevent infectiousness as well as the impact on outcomes based on the timing of distribution and the potential effects of waning immunity. FINDINGS: Increased vaccine efficacy against disease reduces hospitalizations and deaths from COVID-19; however, the relative benefit of transmission blocking varied depending on the timing of vaccine distribution. Early in an outbreak, a vaccine that reduces transmission will be relatively more effective than one introduced later in the outbreak. In addition, earlier and accelerated implementation of a less effective vaccine is more impactful than later implementation of a more effective vaccine. These findings are magnified when considering the durability of the vaccine. Vaccination in the spring will be less impactful when immunity is less durable. INTERPRETATION: Policy choices regarding non-pharmaceutical interventions, such as social distancing and face mask use, will need to remain in place longer if the vaccine is less effective at reducing transmission or distributed slower. In addition, the stage of the local outbreak greatly impacts the overall effectiveness of the vaccine in a region and should be considered when allocating vaccines. FUNDING: Centers for Disease Control and Prevention (CDC) MInD-Healthcare Program (U01CK000589, 1U01CK000536), James S. McDonnell Foundation 21st Century Science Initiative Collaborative Award in Understanding Dynamic and Multiscale Systems, National Science Foundation (CNS-2027908), National Science Foundation Expeditions (CCF1917819), C3.ai Digital Transformation Institute (AWD1006615), and Google, LLC.

Modelling COVID-19 transmission in Africa: countrywise projections of total and severe infections under different lockdown scenarios.

OBJECTIVES: As of 13 January 2021, there have been 3 113 963 confirmed cases of SARS-CoV-2 and 74 619 deaths across the African continent. Despite relatively lower numbers of cases initially, many African countries are now experiencing an exponential increase in case numbers. Estimates of the progression of disease and potential impact of different interventions are needed to inform policymaking decisions. Herein, we model the possible trajectory of SARS-CoV-2 in 52 African countries under different intervention scenarios. DESIGN: We developed a compartmental model of SARS-CoV-2 transmission to estimate the COVID-19 case burden for all African countries while considering four scenarios: no intervention, moderate lockdown, hard lockdown and hard lockdown with continued restrictions once lockdown is lifted. We further analysed the potential impact of COVID-19 on vulnerable populations affected by HIV/AIDS and tuberculosis (TB). RESULTS: In the absence of an intervention, the most populous countries had the highest peaks in active projected number of infections with Nigeria having an estimated 645 081 severe infections. The scenario with a hard lockdown and continued post-lockdown interventions to reduce transmission was the most efficacious strategy for delaying the time to the peak and reducing the number of cases. In South Africa, projected peak severe infections increase from 162 977 to 2 03 261, when vulnerable populations with HIV/AIDS and TB are included in the analysis. CONCLUSION: The COVID-19 pandemic is rapidly spreading across the African continent. Estimates of the potential impact of interventions and burden of disease are essential for policymakers to make evidence-based decisions on the distribution of limited resources and to balance the economic costs of interventions with the potential for saving lives.

Modeling hospital energy and economic costs for COVID-19 infection control interventions.

The study objective assessed the energy demand and economic cost of two hospital-based COVID-19 infection control interventions: negative pressure (NP) treatment rooms and xenon pulsed ultraviolet (XP-UV) equipment. After projecting COVID-19 hospitalizations, a Hospital Energy Model and Infection De-escalation Models quantified increases in energy demand and reductions in infections. The NP intervention was applied to 11, 22, and 44 rooms for small, medium, and large hospitals, while the XP-UV equipment was used eight, nine, and ten hours a day. For small, medium, and large hospitals, the annum kWh for NP rooms were 116,700 kWh, 332,530 kWh, 795,675 kWh, which correspond to annum energy costs of $11,845 ($1,077/room), $33,752 ($1,534/room), and $80,761 ($1,836/room). For XP-UV, the annum-kilowatt-hours (and costs) were 438 ($45), 493 ($50), and 548 ($56) for small, medium, and large hospitals. While energy efficiencies may be expected for the large hospital, the hospital contained more energy-intensive use rooms (ICUs) which resulted in higher operational and energy costs. XP-UV had a greater reduction in secondary COVID-19 infections in large and medium hospitals. NP rooms had a greater reduction in secondary SARS-CoV-2 transmission in small hospitals. Early implementation of interventions can result in realized cost savings through reduced hospital-acquired infections.

Machine learning and artificial intelligence: applications in healthcare epidemiology.

Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.

Optimal Design of Paired Built Environment Interventions for Control of MDROs in Acute Care and Community Hospitals.

OBJECTIVES: Our goal was to optimize infection control of paired environmental control interventions within hospitals to reduce methicillin-resistant Staphylococcus aureus (MRSA), carbapenem-resistant Enterobacteriaceae (CRE), and vancomycin-resistant Enterococci (VRE). BACKGROUND: The most widely used infection control interventions are deployment of handwashing (HW) stations, control of relative humidity (RH), and negative pressure (NP) treatment rooms. Direct costs of multidrug-resistant organism (MDRO) infections are typically not included in the design of such interventions. METHODS: We examined the effectiveness of pairing HW with RH and HW with NP. We used the following three data sets: A meta-analysis of progression rates from uncolonized to colonized to infected, 6 years of MDRO treatment costs from 400 hospitals, and 8 years of MDRO incidence rates at nine army hospitals. We used these data as inputs into an Infection De-Escalation Model with varying budgets to obtain optimal intervention designs. We then computed the infection and prevention rates and cost savings resulting from these designs. RESULTS: The average direct cost of an MDRO infection was $3,289, $1,535, and $1,067 for MRSA, CRE, and VRE. The mean annual incidence rates per facility were 0.39%, 0.034%, and 0.011% for MRSA, CRE, and VRE. After applying the cost-minimizing intervention pair to each scenario, the percentage reductions in infections (and annual direct cost savings) in large, community, and small acute care hospitals were 69% ($1.5 million), 73% ($631K), 60% ($118K) for MRSA, 52% ($460.5K), 58% ($203K), 50% ($37K) for CRE, and 0%, 0%, and 50% ($12.8K) for VRE. CONCLUSION: The application of this Infection De-Escalation Model can guide cost-effective decision making in hospital built environment design to improve control of MDRO infections.

Examining association between cohesion and diversity in collaboration networks of pharmaceutical clinical trials with drug approvals.

OBJECTIVE: Clinical trials ensure that pharmaceutical treatments are safe, efficacious, and effective for public consumption, but are extremely complex, taking up to 10 years and $2.6 billion to complete. One main source of complexity arises from the collaboration between actors, and network science methodologies can be leveraged to explore that complexity. We aim to characterize collaborations between actors in the clinical trials context and investigate trends of successful actors. MATERIALS AND METHODS: We constructed a temporal network of clinical trial collaborations between large and small-size pharmaceutical companies, academic institutions, nonprofit organizations, hospital systems, and government agencies from public and proprietary data and introduced metrics to quantify actors' collaboration network structure, organizational behavior, and partnership characteristics. A multivariable regression analysis was conducted to determine the metrics' relationship with success. RESULTS: We found a positive correlation between the number of successful approved trials and interdisciplinary collaborations measured by a collaboration diversity metric (P < .01). Our results also showed a negative effect of the local clustering coefficient (P < .01) on the success of clinical trials. Large pharmaceutical companies have the lowest local clustering coefficient and more diversity in partnerships across biomedical specializations. CONCLUSIONS: Large pharmaceutical companies are more likely to collaborate with a wider range of actors from other specialties, especially smaller industry actors who are newcomers in clinical research, resulting in exclusive access to smaller actors. Future investigations are needed to show how concentrations of influence and resources might result in diminished gains in treatment development.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support.

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

Biological effects of inhaled hydraulic fracturing sand dust VII. Neuroinflammation and altered synaptic protein expression.

Hydraulic fracturing (fracking) is a process used to recover oil and gas from shale rock formation during unconventional drilling. Pressurized liquids containing water and sand (proppant) are used to fracture the oil- and natural gas-laden rock. The transportation and handling of proppant at well sites generate dust aerosols; thus, there is concern of worker exposure to such fracking sand dusts (FSD) by inhalation. FSD are generally composed of respirable crystalline silica and other minerals native to the geological source of the proppant material. Field investigations by NIOSH suggest that the levels of respirable crystalline silica at well sites can exceed the permissible exposure limits. Thus, from an occupational safety perspective, it is important to evaluate the potential toxicological effects of FSD, including any neurological risks. Here, we report that acute inhalation exposure of rats to one FSD, i.e., FSD 8, elicited neuroinflammation, altered the expression of blood brain barrier-related markers, and caused glial changes in the olfactory bulb, hippocampus and cerebellum. An intriguing observation was the persistent reduction of synaptophysin 1 and synaptotagmin 1 proteins in the cerebellum, indicative of synaptic disruption and/or injury. While our initial hazard identification studies suggest a likely neural risk, more research is necessary to determine if such molecular aberrations will progressively culminate in neuropathology/neurodegeneration leading to behavioral and/or functional deficits.

Molecular beacon anchored onto a graphene oxide substrate.

In this article, we report a graphene oxide-based nanosensor incorporating semiconductor quantum dots linked to DNA-aptamers that functions as a 'turn-off' fluorescent nanosensor for detection of low concentrations of analytes. A specific demonstration of this turn-off aptasensor is presented for the case of the detection of mercury (II) ions. In this system, ensembles of aptamer-based quantum-dot sensors are anchored onto graphene oxide (GO) flakes which provide a platform for analyte detection in the vicinity of GO. Herein, the operation of this ensemble-based nanosensor is demonstrated for mercury ions, which upon addition of mercury, quenching of the emission intensity from the quantum dots is observed due to resonance energy transfer between quantum dots and the gold nanoparticle connected via a mercury target aptamer. A key result is that the usually dominant effect of quenching of the quantum dot due to close proximity to the GO can be reduced to negligible levels by using a linker molecule in conjunctions with the aptamer-based nanosensor. The effect of ionic concentration of the background matrix on the emission intensity was also investigated. The sensor system is found to be highly selective towards mercury and exhibits a linear behavior (r 2 > 0.99) in the nanomolar concentration range. The detection limit of the sensor towards mercury with no GO present was found to be 16.5 nM. With GO attached to molecular beacon via 14 base, 35 base, and 51 base long linker DNA, the detection limit was found to be 38.4 nM, 9.45 nM, and 11.38 nM; respectively.

Modifying welding process parameters can reduce the neurotoxic potential of manganese-containing welding fumes.

Welding fumes (WF) are a complex mixture of toxic metals and gases, inhalation of which can lead to adverse health effects among welders. The presence of manganese (Mn) in welding electrodes is cause for concern about the potential development of Parkinson's disease (PD)-like neurological disorder. Consequently, from an occupational safety perspective, there is a critical need to prevent adverse exposures to WF. As the fume generation rate and physicochemical characteristics of welding aerosols are influenced by welding process parameters like voltage, current or shielding gas, we sought to determine if changing such parameters can alter the fume profile and consequently its neurotoxic potential. Specifically, we evaluated the influence of voltage on fume composition and neurotoxic outcome. Rats were exposed by whole-body inhalation (40 mg/m(3); 3h/day × 5 d/week × 2 weeks) to fumes generated by gas-metal arc welding using stainless steel electrodes (GMA-SS) at standard/regular voltage (25 V; RVSS) or high voltage (30 V; HVSS). Fumes generated under these conditions exhibited similar particulate morphology, appearing as chain-like aggregates; however, HVSS fumes comprised of a larger fraction of ultrafine particulates that are generally considered to be more toxic than their fine counterparts. Paradoxically, exposure to HVSS fumes did not elicit dopaminergic neurotoxicity, as monitored by the expression of dopaminergic and PD-related markers. We show that the lack of neurotoxicity is due to reduced solubility of Mn in HVSS fumes. Our findings show promise for process control procedures in developing prevention strategies for Mn-related neurotoxicity during welding; however, it warrants additional investigations to determine if such modifications can be suitably adapted at the workplace to avert or reduce adverse neurological risks.

Neurotoxicity following acute inhalation of aerosols generated during resistance spot weld-bonding of carbon steel.

Welding generates complex metal aerosols, inhalation of which is linked to adverse health effects among welders. An important health concern of welding fume (WF) exposure is neurological dysfunction akin to Parkinson's disease (PD). Some applications in manufacturing industry employ a variant welding technology known as "weld-bonding" that utilizes resistance spot welding, in combination with adhesives, for metal-to-metal welding. The presence of adhesives raises additional concerns about worker exposure to potentially toxic components like Methyl Methacrylate, Bisphenol A and volatile organic compounds (VOCs). Here, we investigated the potential neurotoxicological effects of exposure to welding aerosols generated during weld-bonding. Male Sprague-Dawley rats were exposed (25 mg/m³ targeted concentration; 4 h/day × 13 days) by whole-body inhalation to filtered air or aerosols generated by either weld-bonding with sparking (high metal, low VOCs; HM) or without sparking (low metal; high VOCs; LM). Fumes generated under these conditions exhibited complex aerosols that contained both metal oxide particulates and VOCs. LM aerosols contained a greater fraction of VOCs than HM, which comprised largely metal particulates of ultrafine morphology. Short-term exposure to LM aerosols caused distinct changes in the levels of the neurotransmitters, dopamine (DA) and serotonin (5-HT), in various brain areas examined. LM aerosols also specifically decreased the mRNA expression of the olfactory marker protein (Omp) and tyrosine hydroxylase (Th) in the olfactory bulb. Consistent with the decrease in Th, LM also reduced the expression of dopamine transporter (Slc6a3; Dat), as well as, dopamine D2 receptor (Drd2) in the olfactory bulb. In contrast, HM aerosols induced the expression of Th and dopamine D5 receptor (Drd5) mRNAs, elicited neuroinflammation and blood-brain barrier-related changes in the olfactory bulb, but did not alter the expression of Omp. Our findings divulge the differential effects of LM and HM aerosols in the brain and suggest that exposure to weld-bonding aerosols can potentially elicit neurotoxicity following a short-term exposure. However, further investigations are warranted to determine if the aerosols generated by weld-bonding can contribute to persistent long-term neurological deficits and/or neurodegeneration.

Manganese accumulation in nail clippings as a biomarker of welding fume exposure and neurotoxicity.

Occupational exposure to welding fumes (WF) is thought to cause Parkinson's disease (PD)-like neurological dysfunction. An apprehension that WF may accelerate the onset of PD also exists. Identifying reliable biomarkers of exposure and neurotoxicity are therefore critical for biomonitoring and neurological risk characterization of WF exposure. Manganese (Mn) in welding consumables is considered the causative factor for the neurological deficits seen in welders. Hence, we sought to determine if Mn accumulation in blood or nail clippings can be a marker for adverse exposure and neurotoxicity. To model this, rats were exposed by intratracheal instillation to dissolved or suspended fume components collected from gas metal arc-mild steel (GMA-MS) or manual metal arc-hard surfacing (MMA-HS) welding. Trace element analysis revealed selective Mn accumulation in dopaminergic brain areas, striatum (STR) and midbrain (MB), following exposure to the two fumes. This caused dopaminergic abnormality as evidenced by loss of striatal tyrosine hydroxylase (Th; 25-32% decrease) and Parkinson disease (autosomal recessive, early onset) 7 (Park7; 25-46% decrease) proteins. While blood Mn was not detectable, Mn levels in nails strongly correlated with the pattern of Mn accumulation in the striatum (R(2)=0.9386) and midbrain (R(2)=0.9332). Exposure to manganese chloride (MnCl(2)) caused similar Mn accumulation in STR, MB and nail. Our findings suggest that nail Mn has the potential to be a sensitive and reliable biomarker for long-term Mn exposure and associated neurotoxicity. The non-invasive means by which nail clippings can be collected, stored, and transported with relative ease, make it an attractive surrogate for biomonitoring WF exposures in occupational settings.